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The Arden Syntax standard for clinical decision support: Experiences and directions
Abstract
Arden Syntax is a widely recognized standard for representing clinical and scientific knowledge in an executable format. It has a history that reaches back until 1989 and is currently maintained by the Health Level 7 (HL7) organization. We created a production-ready development environment, compiler, rule engine and application server for Arden Syntax. Over the course of several years, we have applied this Arden - Syntax - based CDS system in a wide variety of clinical problem domains, such as hepatitis serology interpretation, monitoring of nosocomial infections or the prediction of metastatic events in melanoma patients. We found the Arden Syntax standard to be very suitable for the practical implementation of CDS systems. Among the advantages of Arden Syntax are its status as an actively developed HL7 standard, the readability of the syntax, and various syntactic features such as flexible list handling. A major challenge we encountered was the technical integration of our CDS systems in existing, heterogeneous health information systems. To address this issue, we are currently working on incorporating the HL7 standard GELLO, which provides a standardized interface and query language for accessing data in health information systems. We hope that these planned extensions of the Arden Syntax might eventually help in realizing the vision of a global, interoperable and shared library of clinical decision support knowledge.
... Out of the 19 studies, ve (26%) provided proof of concept regarding the bene ts of deploying rules engines in medical centers (8)(9)(10)(11)(12). Another ve studies (26%) demonstrated the effectiveness of rules engines in improving clinical outcomes (13)(14)(15)(16)(17). Four studies (21%) reported improved performance resulting from the application of rules engines (18)(19)(20)(21). Five studies (26%) designed and implemented e cient CDSS rules engines (10,(22)(23)(24)(25). ...
... b. Utilization of a specialist Health Information Technology (HIT) platform, which may exceed the standard of care in comparison to community-based general physician practices and require more resources from healthcare providers(8, 11 e. Shortcomings of Arden Syntax compared to other approaches, such as the lack of standardized vocabularies and patient data schemas, and di culties in providing actionable choices for clinicians (19). ...
... Among the rules engines identi ed, Drools emerged as the most widely used engine (10,(22)(23)(24) We observed diversity in the use of rules engines, owing to the availability of multiple languages, software, and coding methods for rules. Drools rules engines were the most commonly used, while Arden syntax and Oracle Business Rules (decision tables) were less frequently employed (19,20). Notably, there is a lack of literature on rules engine providers for commercially available systems, limiting the availability of evidence for informed decision-making. ...
Background
The past decade has witnessed significant efforts toward optimizing medical care through the incorporation of technology and artificial intelligence (AI) tools. Rules engines have emerged as key applications in this transformative process, aiming to enhance the quality and efficiency of healthcare systems.
Objective
This scoping review aims to provide a comprehensive overview of the research conducted on rules engines within the medical literature, focusing on their functionalities, the types of tasks they can perform, the evaluated clinical outcomes, and the technologies employed in clinical practice.
Methods
This review adhered to the Arksey and O'Malley framework and followed the PRISMA-ScR checklist (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). A systematic search of the PubMed and Scopus databases was conducted, using specific eligibility criteria. The study included English publications that focused on the utilization of rules engines in medicine.
Results
Nineteen studies met the inclusion and exclusion criteria. The articles exhibited heterogeneity in scope and employed various types of rules engines, encompassing a limited range of medical domains. Several types of rules engines were identified, each contributing to the improvement of clinical outcomes. Descriptive formative designs were employed in ten out of nineteen (53%) articles. The studies primarily targeted chronic diseases and medical practices requiring special attention, such as diabetes mellitus (DM), adverse drug events (ADEs), and pediatric intensive care unit (ICU) settings. The most frequently utilized rules engine was Drools.
Conclusions
The scarcity of published studies on the potential utilization of rules engines in medicine is evident. However, all included studies in this review demonstrated the advantages of incorporating rules engines into medical care, resulting in positive clinical outcomes across various domains. We recommend the adoption of rules engines in healthcare centers, integrating them into daily workflows to deliver convenient, scalable, and effective clinical care. This review serves as a valuable resource for healthcare managers, providers, and patients, facilitating the achievement of more efficient and error-free healthcare environments.
... In this case study, we investigate to which extent the mentioned problems of imperative process models trying to cover all contingencies and, more generally, the medical meaningfulness of alignments can be addressed in a declarative approach. For this purpose, we use the Arden Syntax for Medical Logic Modules (MLMs), which is widely used in medicine and HL7 standardized, to formalize medical knowledge [13]. Besides the classical use of Arden syntax, we show how it can be used as a declarative description of process models by defining rules. ...
... Medical Logic Modules (MLMs) were developed with the aim of presenting medical knowledge in self-contained units, readable by humans and interpretable by computers, and transferable to other clinics [22,23]. The Arden Syntax is a declarative, HL7-standardized, open implementation of MLMs [13,24]. It was developed for the exchange of medical knowledge. ...
... Thus, the guideline is represented by a set of MLMs. MLMs offer the advantage that, despite their expressive power, they are readable in parts even for domain experts after a short training [13]. In the context of clinical guidelines, the expressiveness results from the already existing fields of the MLM structure covering the majority of the information fields of a guideline statement, the extensibility of these by new data fields, and the usability of the Arden Syntax and SQLite. ...
Conformance checking is a process mining technique that allows verifying the conformance of process instances to a given model. Thus, this technique is predestined to be used in the medical context for the comparison of treatment cases with clinical guidelines. However, medical processes are highly variable, highly dynamic, and complex. This makes the use of imperative conformance checking approaches in the medical domain difficult. Studies show that declarative approaches can better address these characteristics. However, none of the approaches has yet gained practical acceptance. Another challenge are alignments, which usually do not add any value from a medical point of view. For this reason, we investigate in a case study the usability of the HL7 standard Arden Syntax for declarative, rule-based conformance checking and the use of manually modeled alignments. Using the approach, it was possible to check the conformance of treatment cases and create medically meaningful alignments for large parts of a medical guideline.
... In this case study, we investigate to which extent the mentioned problems of imperative process models trying to cover all contingencies and, more generally, the medical meaningfulness of alignments can be addressed in a declarative approach. For this purpose, we use the Arden Syntax for Medical Logic Modules (MLMs), which is widely used in medicine and HL7 standardized, to formalize medical knowledge [13]. Besides the classical use of Arden syntax, we show how it can be used as a declarative description of process models by defining rules. ...
... Medical Logic Modules (MLMs) were developed with the aim of presenting medical knowledge in self-contained units, readable by humans and interpretable by computers, and transferable to other clinics [22,23]. The Arden Syntax is a declarative, HL7-standardized, open implementation of MLMs [13,24]. It was developed for the exchange of medical knowledge. ...
... Thus, the guideline is represented by a set of MLMs. MLMs offer the advantage that, despite their expressive power, they are readable in parts even for domain experts after a short training [13]. In the context of clinical guidelines, the expressiveness results from the already existing fields of the MLM structure covering the majority of the information fields of a guideline statement, the extensibility of these by new data fields, and the usability of the Arden Syntax and SQLite. ...
Conformance checking is a process mining technique that allows verifying the conformance of process instances to a given model. Thus, this technique is predestined to be used in the medical context for the comparison of treatment cases with clinical guidelines. However, medical processes are highly variable, highly dynamic, and complex. This makes the use of imperative conformance checking approaches in the medical domain difficult. Studies show that declarative approaches can better address these characteristics. However, none of the approaches has yet gained practical acceptance. Another challenge are alignments, which usually do not add any value from a medical point of view. For this reason, we investigate in a case study the usability of the HL7 standard Arden Syntax for declarative, rule-based conformance checking and the use of manually modeled alignments. Using the approach, it was possible to check the conformance of treatment cases and create medically meaningful alignments for large parts of a medical guideline.
... Samwald et al. [24] discussed the experiences and directions on the recognized Arden standard used in decision support for the representation of available knowledge in an executable format. According to [24] published in 2012, the Arden standard is maintained by the Health Level 7 organization. ...
... Samwald et al. [24] discussed the experiences and directions on the recognized Arden standard used in decision support for the representation of available knowledge in an executable format. According to [24] published in 2012, the Arden standard is maintained by the Health Level 7 organization. Applications of the CDSSs based on Arden syntax cover a wide diversity of problems solving such us: monitoring of nosocomial infections, prediction of metastatic events in melanoma patients, and hepatitis serology interpretation. ...
... There are many studies and researches that prove the high impact of CDSSs. In [24] are presented different experiences regarding clinical decision support and directions of development and research. ...
Many medical diagnosis problems, like rare illnesses and comorbidities, have different kinds of difficulties for medical doctors. Some such difficulties can be handled by clinical decision support systems (CDSSs) that using diverse methods based on artificial intelligence can make useful analyses and processing of clinical data. This paper presents a brief overview of the decision support systems applied in the XXI century medicine by presenting such representative systems with different applications, motivations for their development, and analyzing their impact. Such systems do not substitute the medical doctors they just offer to them support in a taking more efficient decision, which can have improvements like more accurate diagnostics, also making easier the tasks that can be performed by medical doctors. This paper outlines the necessity of endowment of the CDSSs with increased machine intelligence.
... Samwald et al. [24] discussed the experiences and directions on the recognized Arden standard used in decision support for the representation of available knowledge in an executable format. According to [24] published in 2012, the Arden standard is maintained by the Health Level 7 organization. ...
... Samwald et al. [24] discussed the experiences and directions on the recognized Arden standard used in decision support for the representation of available knowledge in an executable format. According to [24] published in 2012, the Arden standard is maintained by the Health Level 7 organization. Applications of the CDSSs based on Arden syntax cover a wide diversity of problems solving such us: monitoring of nosocomial infections, prediction of metastatic events in melanoma patients, and hepatitis serology interpretation. ...
... There are many studies and researches that prove the high impact of CDSSs. In [24] are presented different experiences regarding clinical decision support and directions of development and research. ...
Multe probleme de diagnostic medical, cum ar fi bolile rare și comorbiditățile, presupun diferite tipuri de dificultăți pentru medicii. Unele dintre astfel de dificultăți pot fi rezolvate de sistemele clinice de suport a deciziilor medicale (SCSM), care folosind diverse metode bazate pe inteligență artificială pot efectua diferite prelucrări și analize utile ale datelor clinice. Aceast articol prezintă o scurtă privire de ansamblu asupra sistemelor de suport a deciziilor aplicate în medicina secolului XXI, prezentând astfel de sisteme reprezentative cu diferite aplicații, motivații pentru dezvoltarea lor analizând totodată impactul acestora. Astfel de sisteme nu înlocuiesc medicii ei doar oferă sprijin în luarea unor decizii mai eficiente, ceea ce poate să aducă la îmbunătățiri precum diagnosticare precoce, diagnosticare mai precisă, făcâd de asemenea mai ușoare sarcinile ce trebuie rezolvate de către medici. Totodată este analizată necesitatea înzestrării SCSM cu o inteligență sporită.
... While SQL (Structured Query Language) is widely used for analyzing and extracting data from relational databases [15], it has not been tailored to the complexity and heterogeneity of longitudinal health data [16]. Thus, specialized query languages, such as such as CQL (Clinical Quality Language) for FHIR (Fast Healthcare Interoperability Resources) [17] and AQL (Archetype Query Language) for OpenEHR [18], as well as knowledge processing languages, such as the Arden Syntax [19], have been designed. Although these languages have been tailored towards specific clinical data models, with an emphasis on processing temporal logic and hierarchical data structures, they share with SQL that they support a wide range of transactional and analytical functionalities and have not specifically been designed to solve the challenge addressed in this paper. ...
... Consequently, this has led to the development of specialized medical query languages, such as CQL for FHIR [17] and AQL for OpenEHR [18]. Moreover, medical knowledge processing languages, such as the Arden Syntax can also be used to extract and analyze data [19]. These languages have been designed to address the specific properties of healthcare data, including longitudinality and hierarchical structures. ...
... It utilizes the Arden Syntax [26,80], a widely accepted language for representing medical knowledge, to organize clinical information into modules known as Medical Logic Modules (MLMs). Each MLM encapsulates sufficient knowledge to make a clinical decision [95]. The platform encompasses two essential components [67]: (i) ArdenSuite IDE [66,101], which IDE based on Eclipse IDE facilitating the creation and compilation of MLMs, as well as enabling visual modeling of clinical processes through plugins; (ii) ArdenSuite Server, which is the server-side component that executes compiled MLMs and manages their interactions with other systems, offering a comprehensive API for interoperability [66,68]. ...
Clinical practice guidelines (CPGs) are a formalization of specific clinical knowledge that states the best evidence-based clinical practices for treating pathologies. However, CPGs are limited because they are usually expressed as text. This gives rise to a certain level of ambiguity, subjective interpretation of the actions to be performed, and variability in clinical practice by different health professionals facing the same circumstances. The inherent complexity of CPGs is also a challenge for software engineers designing, developing, and maintaining software systems and clinical decision support system to manage and digitise them. This challenge stems from the need to evolve CPGs and design software systems capable of allowing their evolution. This paper proposes a model-driven, human-centric and tool-supported framework (called IDE 4 ICDS) for improving digitisation of CPG in practical environments. This framework is designed from a human-centric perspective to be used by mixed teams of clinicians and software engineers. It was also validated with the type 2 diabetes mellitus CPG in the Andalusian Public Health System (Spain) involving 89 patients and obtaining a kappa-based analysis. The recommendations were acceptable (0.61 - 0.80) with a total kappa index of 0.701, leading to the conclusion that the proposal provided appropriate recommendations for each patient.
... Another limitation of our algorithms and phenotype algorithms more broadly is limited universal portability. Given the heterogeneous nature of vendor-specific EHR data structures and semantic standards, algorithms cannot be directly executed across EHR types without resource-intensive customisation [40,41]. ...
Background:
Electronic health records can be used for population-wide identification and monitoring of disease. The Territory Kidney Care project developed algorithms to identify individuals with chronic kidney disease (CKD) and several commonly comorbid chronic diseases. This study aims to describe the development and validation of our algorithms for CKD, diabetes, hypertension, and cardiovascular disease. A secondary aim of the study was to describe data completeness of the Territory Kidney Care database.
Methods:
The Territory Kidney Care database consolidates electronic health records from multiple health services including public hospitals (n = 6) and primary care health services (> 60) across the Northern Territory, Australia. Using the database (n = 48,569) we selected a stratified random sample of patients (n = 288), which included individuals with mild to end-stage CKD. Diagnostic accuracy of the algorithms was tested against blinded manual chart reviews. Data completeness of the database was also described.
Results:
For CKD defined as CKD stage 1 or higher (eGFR of any level with albuminuria or persistent eGFR < 60 ml/min/1.732, including renal replacement therapy) overall algorithm sensitivity was 93% (95%CI 89 to 96%) and specificity was 73% (95%CI 64 to 82%). For CKD defined as CKD stage 3a or higher (eGFR < 60 ml/min/1.732) algorithm sensitivity and specificity were 93% and 97% respectively. Among the CKD 1 to 5 staging algorithms, the CKD stage 5 algorithm was most accurate with > 99% sensitivity and specificity. For related comorbidities - algorithm sensitivity and specificity results were 75% and 97% for diabetes; 85% and 88% for hypertension; and 79% and 96% for cardiovascular disease.
Conclusions:
We developed and validated algorithms to identify CKD and related chronic diseases within electronic health records. Validation results showed that CKD algorithms have a high degree of diagnostic accuracy compared to traditional administrative codes. Our highly accurate algorithms present new opportunities in early kidney disease detection, monitoring, and epidemiological research.
Background:
Clinical practice guidelines are systematically developed statements intended to optimize patient care. However, a gap-less implementation of guideline recommendations requires health care personnel not only to be aware of the recommendations and to support their content, but also to recognize every situation in which they are applicable. To not miss situations in which guideline recommendations should be applied, computerized clinical decision support could be given through a system that allows an automated monitoring of adherence to clinical guideline recommendation in individual patients.
Objective:
(1) To collect and analyze the requirements for a system that allows to monitor the adherence to evidence-based clinical guideline recommendations in individual patients, and based on these requirements, (2) to design & implement a software prototype that integrates clinical guideline recommendations with individual patient data and (3) to demonstrate the prototype's utility on a guideline treatment recommendation.
Methods:
We performed a work process analysis with experienced intensive care clinicians to develop a conceptual model of how to support guideline adherence monitoring in clinical routine and identified which steps in the model could be supported electronically. We then identified the core requirements of a software system for supporting recommendation adherence monitoring in a consensus-based requirements analysis within loosely structured focus group work of key stakeholders (clinicians, guideline developers, health data engineers, software developers). Based on these requirements, we designed and implemented a modular system architecture. To demonstrate its utility, we applied the prototype to monitor adherence to a COVID-19 treatment guideline recommendation using clinical data from a large European university hospital.
Results:
We have designed a system that integrates guideline recommendations with real-time clinical data to evaluate individual guideline recommendation adherence, and developed a functional prototype. The needs analysis with clinical staff resulted in a flow chart describing the work process of how the adherence to guideline recommendations should be monitored. Four core requirements were identified, including the ability to decide whether a guideline recommendation is applicable and implemented for a specific patient, the ability to integrate clinical data from different data formats and data structures, the ability to display raw patient data, and the use of a FHIR-based format for the representation of clinical practice guideline recommendations to provide an interoperable, standards-based guideline recommendation exchange format.
Conclusions:
Our system holds advantages for individual patient treatment and quality management in hospitals. However, further studies are needed to measure the impact on patient outcomes and evaluate its resource-effectiveness in different clinical settings. We specified a modular software architecture that allows experts from different fields to work independently and focus on their area of expertise. We have released the source code of our system under an open-source license and invite for cooperation and collaborative further development of the system.
The inductive miner (IM) can guarantee to return structured process models, but the process behaviours that process trees can represent are limited. Loops in process trees can only be exited after the execution of the “body” part. However, in some cases, it is possible to break a loop structure in the “redo” part. This paper proposes an extension to the process tree notation and the IM to discover and represent break behaviours. We present a case study using a healthcare event log to explore Acute Coronary Syndrome (ACS) patients’ treatment pathways, especially discharge behaviours from ICU, to demonstrate the usability of the proposed approach in real-life. We find that treatment pathways in ICU are routine behaviour, while discharges from ICU are break behaviours. The results show that we can successfully discover break behaviours and obtain the structured and understandable process model with satisfactory fitness, precision and simplicity.
Event logs capture information about executed activities. However, they do not capture information about activities that could have been performed, i.e., activities that were enabled during a process. Event logs containing information on enabled activities are called translucent event logs. Although it is possible to extract translucent event logs from a running information system, such logs are rarely stored. To increase the availability of translucent event logs, we propose two techniques. The first technique records the system’s states as snapshots. These snapshots are stored and linked to events. A user labels patterns that describe parts of the system’s state. By matching patterns with snapshots, we can add information about enabled activities. We apply our technique in a small setting to demonstrate its applicability. The second technique uses a process model to add information concerning enabled activities to an existing traditional event log. Data containing enabled activities are valuable for process discovery. Using the information on enabled activities, we can discover more correct models.
IoT devices supporting business processes (BPs) in sectors like manufacturing, logistics or healthcare collect data on the execution of the processes. In the last years, there has been a growing awareness of the opportunity to use the data these devices generate for process mining (PM) by deriving an event log from a sensor log via event abstraction techniques. However, IoT data are often affected by data quality issues (e.g., noise, outliers) which, if not addressed at the preprocessing stage, will be amplified by event abstraction and result in quality issues in the event log (e.g., incorrect events), greatly hampering PM results. In this paper, we review the literature on PM with IoT data to find the most frequent data quality issues mentioned in the literature. Based on this, we then derive six patterns of poor sensor data quality that cause event log quality issues and propose solutions to avoid or solve them.
Human behavior could be represented in the form of a process. Existing process modeling notations, however, are not able to faithfully represent these very flexible and unstructured processes. Additional non-process aware perspectives should be considered in the representation. Control-flow and data dimensions should be combined to build a robust model which can be used for analysis purposes. The work in this paper proposes a new hybrid model in which these dimensions are combined. An enriched conformance checking approach is described, based on the alignment of imperative and declarative process models, which also supports data dimensions from a statistical viewpoint.
Object-centric event log is a format for properly organizing information from different views of a business process into an event log. The novelty in such a format is the association of events with objects, which allows different notions of cases to be analyzed. The addition of new features has brought an increase in complexity. Clustering analysis can ease this complexity by enabling the analysis to be guided by process behaviour profiles. However, identifying which features describe the singularity of each profile is a challenge. In this paper, we present an exploratory study in which we mine frequent patterns on top of clustering analysis as a mechanism for profile characterization. In our study, clustering analysis is applied in a trace clustering fashion over a vector representation for a flattened event log extracted from an object-centric event log, using a unique case notion. Then, frequent patterns are discovered in the event sublogs associated with clusters and organized according to that original object-centric event log. The results obtained in preliminary experiments show association rules reveal more evident behaviours in certain profiles. Despite the process underlying each cluster may contain the same elements (activities and transitions), the behaviour trends show the relationships between such elements are supposed to be different. The observations depicted in our analysis make room to search for subtler knowledge about the business process under scrutiny.
Process mining is a family of techniques that support the analysis of operational processes based on event logs. Among the existing event log formats, the IEEE standard eXtensible Event Stream () is the most widely adopted. In , each event must be related to a single case object, which may lead to convergence and divergence problems. To solve such issues, object-centric approaches become promising, where objects are the central notion and one event may refer to multiple objects. In particular, the Object-Centric Event Logs () standard has been proposed recently. However, the crucial problem of extracting logs from external sources is still largely unexplored. In this paper, we try to fill this gap by leveraging the Virtual Knowledge Graph () approach to access data in relational databases. We have implemented this approach in the system, extending it to support both and standards. We have carried out an experiment with over the Dolibarr system. The evaluation results confirm that can effectively extract logs and the performance is scalable.
Predictive process monitoring techniques leverage machine learning (ML) to predict future characteristics of a case, such as the process outcome or the remaining run time. Available techniques employ various models and different types of input data to produce accurate predictions. However, from a practical perspective, explainability is another important requirement besides accuracy since predictive process monitoring techniques frequently support decision-making in critical domains. Techniques from the area of explainable artificial intelligence (XAI) aim to provide this capability and create transparency and interpretability for black-box ML models. While several explainable predictive process monitoring techniques exist, none of them leverages textual data. This is surprising since textual data can provide a rich context to a process that numerical features cannot capture. Recognizing this, we use this paper to investigate how the combination of textual and non-textual data can be used for explainable predictive process monitoring and analyze how the incorporation of textual data affects both the predictions and the explainability. Our experiments show that using textual data requires more computation time but can lead to a notable improvement in prediction quality with comparable results for explainability.
Aggregation of event data is a key operation in process mining for revealing behavioral features of processes for analysis. It has primarily been studied over sequences of events in event logs. The data model of event knowledge graphs enables new analysis questions requiring new forms of aggregation. We focus on analyzing task executions in event knowledge graphs. We show that existing aggregation operations are inadequate and propose new aggregation operations, formulated as query operators over labeled property graphs. We show on the BPIC’17 dataset that the new aggregation operations allow gaining new insights into differences in task executions, actor behavior, and work division.
During the last years, a number of studies have experimented with applying process mining (PM) techniques to smart spaces data. The general goal has been to automatically model human routines as if they were business processes. However, applying process-oriented techniques to smart spaces data comes with its own set of challenges. This paper surveys existing approaches that apply PM to smart spaces and analyses how they deal with the following challenges identified in the literature: choosing a modelling formalism for human behaviour; bridging the abstraction gap between sensor and event logs; and segmenting logs in traces. The added value of this article lies in providing the research community with a common ground for some important challenges that exist in this field and their respective solutions, and to assist further research efforts by outlining opportunities for future work.
Public event logs are valuable for process mining research to evaluate process mining artifacts and identify new and promising research directions. Initiatives like the BPI Challenges have provided a series of real-world event logs, including healthcare processes, and have significantly stimulated process mining research. However, the healthcare related logs provide only excerpts of patient visits in hospitals. The Medical Information Mart for Intensive Care (MIMIC)-IV database is a public available relational database that includes data on patient treatment in a tertiary academic medical center in Boston, USA. It provides complex care processes in a hospital from end-to-end. To facilitate the use of MIMIC-IV in process mining and to increase the reproducibility of research with MIMIC, this paper provides a framework consisting of a method, an event hierarchy, and a log extraction tool for extracting useful event logs from the MIMIC-IV database. We demonstrate the framework on a heart failure treatment process, show how logs on different abstraction levels can be generated, and provide configuration files to generate event logs of previous process mining works with MIMIC.
Anomaly detection can identify deviations in event logs and allows businesses to infer inconsistencies, bottlenecks, and optimization opportunities in their business processes. In recent years, various anomaly detection algorithms for business processes have been proposed based on either process discovery or machine learning algorithms. While there are apparent differences between machine learning and process discovery approaches, it is often unclear how they perform in comparison. Furthermore, deep learning research in other domains has shown that advancements did not solely come from improved model architecture but were often due to minor pre-processing and training procedure refinements. For this reason, this paper aims to set up a broad benchmark and establish a baseline for deep learning-based anomaly detection of process instances. To this end, we introduce a simple LSTM-based anomaly detector utilizing a collection of minor refinements and compare it with existing approaches. The results suggest that the proposed method can significantly outperform the existing approaches on a large number of event logs consistently.
When multiple objects are involved in a process, there is an opportunity for processes to be discovered from different angles with new information that previously might not have been analyzed from a single object point of view. This does require that all the information of event/object attributes and their values are stored within logs including attributes that have a list of values or attributes with values that change over time. It also requires that attributes can unambiguously be linked to an object, an event or both. As such, object-centric event logs are an interesting development in process mining as they support the presence of multiple types of objects. First, this paper shows that the current object-centric event log formats do not support the aforementioned aspects to their full potential since the possibility to support dynamic object attributes (attributes with changing values) is not supported by existing formats. Next, this paper introduces a novel enriched object-centric event log format tackling the aforementioned issues alongside an algorithm that automatically translates XES logs to this Data-aware OCEL (DOCEL) format.
Process discovery is a family of techniques that helps to comprehend processes from their data footprints. Yet, as processes change over time so should their corresponding models, and failure to do so will lead to models that under- or over-approximate behaviour. We present a discovery algorithm that extracts declarative processes as Dynamic Condition Response (DCR) graphs from event streams. Streams are monitored to generate temporal representations of the process, later processed to create declarative models. We validated the technique by identifying drifts in a publicly available dataset of event streams. The metrics extend the Jaccard similarity measure to account for process change in a declarative setting. The technique and the data used for testing are available online.
Organizations increasingly use process mining techniques to gain insight into their processes. Process mining techniques can be used to monitor and/or enhance processes. However, the impact of processes on the people involved, in terms of unfair discrimination, has not been studied. Another neglected area is the impact of applying process mining techniques on the fairness of processes. In this paper, we overview and categorize the existing fairness concepts in machine learning. Moreover, we summarize the areas where fairness is relevant to process mining and provide an approach to applying existing fairness definitions in process mining. Finally, we present some of the fairness-related challenges in processes.
Analysing the treatment pathways in real-world health data can provide valuable insight for clinicians and decision-makers. However, the procedures for acquiring real-world data for research can be restrictive, time-consuming and risks disclosing identifiable information. Synthetic data might enable representative analysis without direct access to sensitive data. In the first part of our paper, we propose an approach for grading synthetic data for process analysis based on its fidelity to relationships found in real-world data. In the second part, we apply our grading approach by assessing cancer patient pathways in a synthetic healthcare dataset (The Simulacrum provided by the English National Cancer Registration and Analysis Service) using process mining. Visualisations of the patient pathways within the synthetic data appear plausible, showing relationships between events confirmed in the underlying non-synthetic data. Data quality issues are also present within the synthetic data which reflect real-world problems and artefacts from the synthetic dataset’s creation. Process mining of synthetic data in healthcare is an emerging field with novel challenges. We conclude that researchers should be aware of the risks when extrapolating results produced from research on synthetic data to real-world scenarios and assess findings with analysts who are able to view the underlying data.
A lot of recent literature on outcome-oriented predictive process monitoring focuses on using models from machine and deep learning. In this literature, it is assumed the outcome labels of the historical cases are all known. However, in some cases, the labelling of cases is incomplete or inaccurate. For instance, you might only observe negative customer feedback, fraudulent cases might remain unnoticed. These cases are typically present in the so-called positive and unlabelled (PU) setting, where your data set consists of a couple of positively labelled examples and examples which do not have a positive label, but might still be examples of a positive outcome. In this work, we show, using a selection of event logs from the literature, the negative impact of mislabelling cases as negative, more specifically when using XGBoost and LSTM neural networks. Furthermore, we show promising results on real-life datasets mitigating this effect, by changing the loss function used by a set of models during training to those of unbiased Positive-Unlabelled (uPU) or non-negative Positive-Unlabelled (nnPU) learning.
Assigning resources in business processes execution is a repetitive task that can be effectively automated. However, different automation methods may give varying results that may not be optimal. Proper resource allocation is crucial as it may lead to significant cost reductions or increased effectiveness that results in increased revenues.
In this work, we first propose a novel representation that allows the modeling of a multi-process environment with different process-based rewards. These processes can share resources that differ in their eligibility. Then, we use double deep reinforcement learning to look for an optimal resource allocation policy. We compare those results with two popular strategies that are widely used in the industry. Learning optimal policy through reinforcement learning requires frequent interactions with the environment, so we also designed and developed a simulation engine that can mimic real-world processes.
The results obtained are promising. Deep reinforcement learning based resource allocation achieved significantly better results compared to two commonly used techniques.
Customer journey analysis is important for organizations to get to know as much as possible about the main behavior of their customers. This provides the basis to improve the customer experience within their organization. This paper addresses the problem of predicting the occurrence of a certain activity of interest in the remainder of the customer journey that follows the occurrence of another specific activity. For this, we propose the HIAP framework which uses process mining techniques to analyze customer journeys. Different prediction models are researched to investigate which model is most suitable for high importance activity prediction. Furthermore the effect of using a sliding window or landmark model for (re)training a model is investigated. The framework is evaluated using a health insurance real dataset and a benchmark data set. The efficiency and prediction quality results highlight the usefulness of the framework under various realistic online business settings.
In recent years, AutoML has emerged as a promising technique for reducing computational and time cost by automating the development of machine learning models. Existing AutoML tools cannot be applied directly to process predictive monitoring (PPM), because they do not support several configuration parameters that are PPM-specific, such as trace bucketing or encoding. In other words, they are only specialized in finding the best configuration of machine learning model hyperparameters. In this paper, we present a simple yet extensible framework for AutoML in PPM. The framework uses genetic algorithms to explore a configuration space containing both PPM-specific parameters and the traditional machine learning model hyperparameters. We design four different types of experiments to verify the effectiveness of the proposed approach, comparing its performance in respect of random search of the configuration space, using two publicly available event logs. The results demonstrate that the proposed approach outperforms consistently the random search.
In this paper, we introduce the SAP Signavio Academic Models (SAP-SAM) dataset, a collection of hundreds of thousands of business models, mainly process models in BPMN notation. The model collection is a subset of the models that were created over the course of roughly a decade on academic.signavio.com , a free-of-charge software-as-a-service platform that researchers, teachers, and students can use to create business (process) models. We provide a preliminary analysis of the model collection, as well as recommendations on how to work with it. In addition, we discuss potential use cases and limitations of the model collection from academic and industry perspectives.
We present a method and prototype tool supporting participatory mapping of domain activities to event data recorded in information systems via the system interfaces. The aim is to facilitate responsible secondary use of event data recorded in information systems, such as process mining and the construction of predictive AI models. Another identified possible benefit is the support for increasing the quality of data by using the mapping to support educating new users in how to register data, thereby increasing the consistency in how domain activities are recorded. We illustrate the method on two cases, one from a job center in a danish municipality and another from a danish hospital using the healthcare platform from Epic.
This paper presents an approach of using methods of process mining and rule-based artificial intelligence to analyze and understand study paths of students based on campus management system data and study program models. Process mining techniques are used to characterize successful study paths, as well as to detect and visualize deviations from expected plans. These insights are combined with recommendations and requirements of the corresponding study programs extracted from examination regulations. Here, event calculus and answer set programming are used to provide models of the study programs which support planning and conformance checking while providing feedback on possible study plan violations. In its combination, process mining and rule-based artificial intelligence are used to support study planning and monitoring by deriving rules and recommendations for guiding students to more suitable study paths with higher success rates. Two applications will be implemented, one for students and one for study program designers.
Constraint monitoring aims to monitor the violation of constraints in business processes, e.g., an invoice should be cleared within 48 h after the corresponding goods receipt, by analyzing event data. Existing techniques for constraint monitoring assume that a single case notion exists in a business process, e.g., a patient in a healthcare process, and each event is associated with the case notion. However, in reality, business processes are object-centric , i.e., multiple case notions (objects) exist, and an event may be associated with multiple objects. For instance, an Order-To-Cash (O2C) process involves order , item , delivery , etc., and they interact when executing an event, e.g., packing multiple items together for a delivery. The existing techniques produce misleading insights when applied to such object-centric business processes. In this work, we propose an approach to monitoring constraints in object-centric business processes. To this end, we introduce Object-Centric Constraint Graphs (OCCGs) to represent constraints that consider the interaction of objects. Next, we evaluate the constraints represented by OCCGs by analyzing Object-Centric Event Logs (OCELs) that store the interaction of different objects in events. We have implemented a web application to support the proposed approach and conducted two case studies using a real-life SAP ERP system.
Computer-based education relies on information systems to support teaching and learning processes. These systems store trace data about the interaction of the learners with their different functionalities. Process mining techniques have been used to evaluate these traces and provide insights to instructors on the behavior of students. However, an analysis of students behavior on solving open-questioned examinations combined with the marks they received is still missing. This analysis can support the instructors not only on improving the design of future edition of the course, but also on improving the structure of online and physical evaluations. In this paper, we use process mining techniques to evaluate the behavioral patterns of students solving computer-based open-ended exams and their correlation with the grades. Our results show patterns of behavior associated to the marks received. We discuss how these results may support the instructor on elaborating future open question examinations.
In recent years, hospitals and other care providers in the Netherlands are coping with a widespread nursing shortage and a directly related increase in nursing workload. This nursing shortage combined with the high nursing workload is associated with higher levels of burnout and reduced job satisfaction among nurses. However, not only the nurses, but also the patients are affected as an increasing nursing workload adversely affects patient safety and satisfaction. Therefore, the aim of this research is to predict the care acuity corresponding to an individual patient for the next admission day, by using the available structured hospital data of the previous admission days. For this purpose, we make use of an LSTM model that is able to predict the care acuity of the next day, based on the hospital data of all previous days of an admission. In this paper, we elaborate on the architecture of the LSTM model and we show that the prediction accuracy of the LSTM model increases with the increase of the available amount of historical event data. We also show that the model is able to identify care acuity differences in terms of the amount of support needed by the patient. Moreover, we discuss how the predictions can be used to identify which patient care related characteristics and different types of nursing activities potentially contribute to the care acuity of a patient.
Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted.
The discipline of process mining has a solid track record of successful applications to the healthcare domain. Within such research space, we conducted a case study related to the Intensive Care Unit (ICU) ward of the Uniklinik Aachen hospital in Germany. The aim of this work is twofold: developing a normative model representing the clinical guidelines for the treatment of COVID-19 patients, and analyzing the adherence of the observed behavior (recorded in the information system of the hospital) to such guidelines. We show that, through conformance checking techniques, it is possible to analyze the care process for COVID-19 patients, highlighting the main deviations from the clinical guidelines. The results provide physicians with useful indications for improving the process and ensuring service quality and patient satisfaction. We share the resulting model as an open-source BPMN file.
Conformance checking is a process mining technique that allows verifying the conformance of process instances to a given model. Many conformance checking algorithms provide quantitative information about the conformance of a process instance through metrics such as fitness. Fitness measures to what degree the model allows the behavior observed in the event log. Conventional fitness does not consider the individual severity of deviations. In cases where there are rules that are more important to comply with than others, fitness consequently does not take all factors into account. In the field of medicine, for example, there are guideline recommendations for clinical treatment that have information about their importance and soundness, making it essential to distinguish between them. Therefore, we introduce an alignment-based conformance checking approach that considers the importance of individual specifications and weights violations. The approach is evaluated with real patient data and evidence-based guideline recommendations. Using this approach, it was possible to integrate guideline recommendation metadata into the conformance checking process and to weight violations individually.
In the area of industrial process mining, privacy-preserving event data publication is becoming increasingly relevant. Consequently, the trade-off between high data utility and quantifiable privacy poses new challenges. State-of-the-art research mainly focuses on differentially private trace variant construction based on prefix expansion methods. However, these algorithms face several practical limitations such as high computational complexity, introducing fake variants, removing frequent variants, and a bounded variant length. In this paper, we introduce a new approach for direct differentially private trace variant release which uses anonymized partition selection strategies to overcome the aforementioned restraints. Experimental results on real-life event data show that our algorithm outperforms state-of-the-art methods in terms of both plain data utility and result utility preservation.
Process mining is a set of techniques that are used by organizations to understand and improve their operational processes. The first essential step in designing any process reengineering procedure is to find process improvement opportunities. In existing work, it is usually assumed that the set of problematic process instances in which an undesirable outcome occurs is known prior or is easily detectable. So the process enhancement procedure involves finding the root causes and the treatments for the problem in those process instances. For example, the set of problematic instances is considered as those with outlier values or with values smaller/bigger than a given threshold in one of the process features. However, on various occasions, using this approach, many process enhancement opportunities, not captured by these problematic process instances, are missed. To overcome this issue, we formulate finding the process enhancement areas as a context-sensitive anomaly/outlier detection problem. We define a process enhancement area as a set of situations (process instances or prefixes of process instances) where the process performance is surprising. We aim to characterize those situations where process performance is significantly different from what was expected considering its performance in similar situations. To evaluate the validity and relevance of the proposed approach, we have implemented and evaluated it on a real-life event log.
To improve the user experience, service providers may systematically record and analyse user interactions with a service using event logs. User journeys model these interactions from the user’s perspective. They can be understood as event logs created by two independent parties, the user and the service provider, both controlling their share of actions. We propose multi-party event logs as an extension of event logs with information on the parties, allowing user journeys to be analysed as weighted games between two players. To reduce the size of games for complex user journeys, we identify decision boundaries at which the outcome of the game is determined. Decision boundaries identify subgames that are equivalent to the full game with respect to the final outcome of user journeys. The decision boundary analysis from multi-party event logs has been implemented and evaluated on the BPI Challenge 2017 event log with promising results, and can be connected to existing process mining pipelines.
Data and process mining techniques can be applied in many areas to gain valuable insights. For many reasons, accessibility to real-world business and medical data is severely limited. However, research, but especially the development of new methods, depends on a sufficient basis of realistic data. Due to the lack of data, this progress is hindered. This applies in particular to domains that use personal data, such as healthcare. With adequate quality, synthetic data can be a solution to this problem. In the procedural field, some approaches have already been presented that generate synthetic data based on a process model. However, only a few have included the data perspective so far. Data semantics, which is crucial for the quality of the generated data, has not yet been considered. Therefore, in this paper we present the multi-perspective event log generation approach SAMPLE that considers the data perspective and, in particular, its semantics. The evaluation of the approach is based on a process model for the treatment of malignant melanoma. As a result, we were able to integrate the semantic of data into the log generation process and identify new challenges.
The widespread implementation of intelligent decision support systems (IDSS) is hampered by the lack of methods and technologies for automatically filling the knowledge base during the operation of such systems. This problem is especially acute in the medical field. Its solution lies in the application of automatic planning technologies. The methods and algorithms developed in this field for estimation the optimal strategy for solving problems, which are strictly formulated in terms of predicate logic, allow numerically evaluating the usefulness of new messages and thus ranking information by importance and automatically selecting essential information for entering it into the knowledge base. The paper proposes the architecture of a medical IDSS that implements this approach, substantiates the applicability of the Markov approximation for the formalization of automatic planning tasks in the medical field, shows the effectiveness of the proposed approach using the example of an informed choice of serum for influenza vaccination.
Patient-oriented data-driven CDSS architecture, based on adaptive ontology, is proposed as a perspective for a future development of intelligent medical decision support systems. A human body (anatomy and physiology) knowledge base should be the basic component of the system with the possibility to permanently automated update the deeply structured data, both general and personal, using the technologies of ontology learning, natural language processing, and automated planning. Already existing information technologies, standards, and protocols allow implementing such an approach in a healthcare domain in a framework of FHIR HL7.org standard.
Background
Artificial intelligence (AI) is one of the newest fields in science and engineering. It refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. Artificial intelligence as a science is very broad and encompasses various fields, including reasoning, natural language processing, planning, and machine learning. In modern times the real-world current applications of AI include health care, automotive, finance and economics, playing video games, solving mathematical theorems, writing poetry, driving a car on a crowded street, and many more all of which aim to improve human life.
Methods
The aim of this article is to review the current application of AI in the field of dentistry based on electronic search in various data bases like Google scholar, PubMed, and Scopus.
Results
The present review outlines the potential applications of AI in the field of Dentistry in diagnosis, treatment planning, and disease prediction and discusses its impact on dentists, with the objective of creating a support for future research in this rapidly expanding arena.
Conclusions
Artificial intelligence systems can simplify the tasks, give a standardization to the procedures and provide results in quick time which can save the dentist time and help the dentist to perform his duties more efficiently.
We describe a software architecture for clinical decision support systems. The core of the architecture is the Health Level Seven (HL7) Arden Syntax. Functionality and scalability of this architecture were proven in large routine applications for early detection and automated monitoring of hospital acquired infections at the Vienna General Hospital.These systems automatically receive data from 15 intensive care units (ICUs) and generate indications as to whether various forms of ICU acquired infections are clearly present in a patient, present to a certain degree, or absent.
Nosocomial, or hospital-acquired, infections (NIs) are a frequent complication affecting hospitalized patients. The growing
availability of computerized patient records in hospitals allows automated identification and extended monitoring of the signs
of NI for the purpose of reducing NI rates. A fuzzy- and knowledge-based system to identify and monitor NIs at intensive care
units according to the European Surveillance System HELICS was developed. It was implemented into the information technology
landscape of the Vienna General Hospital and is now in routine use.
Expert surveillance of healthcare-associated infections (HCAIs) is a key parameter for good clinical practice, especially in intensive care medicine. Assessment of clinical entities such as HCAIs is a time-consuming task for highly trained experts. Such are neither available nor affordable in sufficient numbers for continuous surveillance services. Intelligent information technology (IT) tools are in urgent demand.
MONI-ICU (monitoring of nosocomial infections in intensive care units (ICUs)) has been developed methodologically and practically in a stepwise manner and is a reliable surveillance IT tool for clinical experts. It uses information from the patient data management systems in the ICUs, the laboratory information system, and the administrative hospital information system of the Vienna General Hospital as well as medical expert knowledge on infection criteria applied in a multilevel approach which includes fuzzy logic rules.
We describe the use of this system in clinical routine and compare the results generated automatically by MONI-ICU with those generated in parallel by trained surveillance staff using patient chart reviews and other available information ("gold standard"). A total of 99 ICU patient admissions representing 1007 patient days were analyzed. MONI-ICU identified correctly the presence of an HCAI condition in 28/31 cases (sensitivity, 90.3%) and their absence in 68/68 of the non-HCAI cases (specificity, 100%), the latter meaning that MONI-ICU produced no "false alarms". The 3 missed cases were due to correctable technical errors. The time taken for conventional surveillance at the 52 ward visits was 82.5 hours. MONI-ICU analysis of the same patient cases, including careful review of the generated results, required only 12.5 hours (15.2%).
Provided structured and sufficient information on clinical findings is online available, MONI-ICU provides an almost real-time view of clinical indicators for HCAI - at the cost of almost no additional time on the part of surveillance staff or clinicians.
The Arden Syntax for Medical Logic Systems is a standard for clinical knowledge representation, maintained by the Health Level Seven (HL7) organization and approved by the American National Standards Institute (ANSI). It offers a wide range of syntactical constructs (various forms of numer-ical, logical, temporal operators, conditions, …), each of which crisply defines a specific unit of clinical knowledge (yes-no evaluations). As medical conditions and conclusions cannot always be formulated in a strict manner, methods of fuzzy set theory and logic are used to represent uncer-tainty, which is usually a part of practical clinical knowledge. Based on the extension of Arden Syntax to Fuzzy Arden Syntax by Vetterlein et al. (on the basis of Tiffe's earlier extension), we im-plemented a Fuzzy Arden Syntax compiler which is able to process a fully fuzzified version of Ar-den Syntax. We describe the compiler, its components (lexer, parser, and synthesis), and discuss its implementation.
The Guideline Interchange Format (GLIF) and the Arden Syntax are methodologies that were created for the purpose of sharing certain kinds of medical knowledge. While the Arden Syntax is already a standard of the American Society for Testing and Materials (ASTM) and has been used to implement medical decision rules, GLIF is an evolving methodology for representing the logic and flow of clinical guidelines. In this paper we seek to define the relationship between GLIF and the Arden Syntax, highlighting the complementary role that they play in sharing medical knowledge. While the Arden Syntax was designed to represent single decision rules in self-contained units called Medical Logic Modules (MLMs), GLIF specifies entire guidelines that are generally intended to unfold over time. An MLM has a single specification that is computable, while guideline development in GLIF can be done at three different levels of abstraction: an conceptual flowchart of medical decisions and actions, a computable specification that includes well-defined delineation of decision criteria and patient data, and an implementable specification, that includes local adaptations and mapping of guideline variables to institutional databases. The current version of GLIF uses a superset of the Arden Syntax logic grammar to specify logical and temporal decision criteria, but includes additional constructs that support other elements required by complex clinical guidelines. GLIF also includes an MLMmacro class that enables mapping of guideline recommendations into MLMs, in cases where the implementing institution wishes to use MLMs. 1.
Despite their potential to significantly improve health care, advanced clinical decision support (CDS) capabilities are not widely available in the clinical setting. An important reason for this limited availability of CDS capabilities is the application-specific and institution-specific nature of most current CDS implementations. Thus, a critical need for enabling CDS capabilities on a much larger scale is the development and adoption of standards that enable current and emerging CDS resources to be more effectively leveraged across multiple applications and care settings. Standards required for such effective scaling of CDS include (i) standard terminologies and information models to represent and communicate about health care data; (ii) standard approaches to representing clinical knowledge in both human-readable and machine-executable formats; and (iii) standard approaches for leveraging these knowledge resources to provide CDS capabilities across various applications and care settings. A number of standards do exist or are under development to meet these needs. However, many gaps and challenges remain, including the excessive complexity of many standards; the limited availability of easily accessible knowledge resources implemented using standard approaches; and the lack of tooling and other practical resources to enable the efficient adoption of existing standards. Thus, the future development and widespread adoption of current CDS standards will depend critically on the availability of tooling, knowledge bases, and other resources that make the adoption of CDS standards not only the right approach to take, but the cost-effective path to follow given the alternative of using a traditional, ad hoc approach to implementing CDS.
The programming language Arden Syntax is especially adapted to the needs of computer-based clinical decision support. Recently, an extension of Arden Syntax, named Fuzzy Arden Syntax, was proposed by the authors. Fuzzy Arden Syntax is a conservative extension of Arden Syntax and offers special functionality to process gradual information. The background is the observation that in medicine we frequently deal with statements which are neither clearly false nor clearly true but hold to some intermediate degree. In this paper, we demonstrate under which circumstances a Medical Logic Module (a program unit written in Arden Syntax) may show unintended behavior and how the situation can easily be improved by means of the possibilities offered by Fuzzy Arden Syntax. To this end, an example from the domain of nosocomial infection control is discussed in detail.
Nosocomial or hospital-acquired infections (NIs) are a frequent complication in hospitalized patients. The growing availability of computerized patient records in hospitals permits automated identification and extended monitoring for signs of NIs. A fuzzy- and knowledge-based system to identify and monitor NIs at intensive care units (ICUs) according to the European Surveillance System HELICS (NI definitions derived from the Centers of Disease Control and Prevention (CDC) criteria) was developed and put into operation at the Vienna General Hospital. This system, named Moni, for monitoring of nosocomial infections contains medical knowledge packages (MKPs) to identify and monitor various infections of the bloodstream, pneumonia, urinary tract infections, and central venous catheter-associated infections. The MKPs consist of medical logic modules (MLMs) in Arden syntax, a medical knowledge representation scheme, whose definition is part of the HL7 standards. These MLM packages together with the Arden software are well suited to be incorporated in medical information systems such as hospital information or intensive-care patient data management systems, or in web-based applications. In terms of method, Moni contains an extended data-to-symbol conversion with several layers of abstraction, until the top level defining NIs according to HELICS is reached. All included medical concepts such as "normal", "increased", "decreased", or similar ones are formally modeled by fuzzy sets, and fuzzy logic is used to process the interpretations of the clinically observed and measured patient data through an inference network. The currently implemented cockpit surveillance connects 96 ICU beds with Moni and offers the hospital's infection control department a hitherto unparalleled NI infection survey.
Arden Syntax is a Health Level Seven (HL7) standard that can be used to encode computable knowledge. However, dissemination of knowledge is hampered by lack of standard database linkages in Arden knowledge bases (KB). Moreover, the HL7 Reference Information Model (RIM) is object-oriented and hence incompatible with the current Arden data model. Also, significant investment has been made in Arden KBs that would be lost if a backward-incompatible data model were adopted.
To define a data model that standardizes database linkages and provides object-oriented features while maintaining backward compatibility.
We identified the objects of the RIM that could be used as a schema for standard database queries. We propose extensions to Arden to accommodate this model, including the manipulation of objects.
A data model that standardizes database linkages and introduces object-oriented constructs will facilitate knowledge transfer without violation of backward compatibility in the Arden Syntax.
The Guideline Interchange Format (GLIF) is a model for representation of sharable computer-interpretable guidelines. The current version of GLIF (GLIF3) is a substantial update and enhancement of the model since the previous version (GLIF2). GLIF3 enables encoding of a guideline at three levels: a conceptual flowchart, a computable specification that can be verified for logical consistency and completeness, and an implementable specification that is intended to be incorporated into particular institutional information systems. The representation has been tested on a wide variety of guidelines that are typical of the range of guidelines in clinical use. It builds upon GLIF2 by adding several constructs that enable interpretation of encoded guidelines in computer-based decision-support systems. GLIF3 leverages standards being developed in Health Level 7 in order to allow integration of guidelines with clinical information systems. The GLIF3 specification consists of an extensible object-oriented model and a structured syntax based on the resource description framework (RDF). Empirical validation of the ability to generate appropriate recommendations using GLIF3 has been tested by executing encoded guidelines against actual patient data. GLIF3 is accordingly ready for broader experimentation and prototype use by organizations that wish to evaluate its ability to capture the logic of clinical guidelines, to implement them in clinical systems, and thereby to provide integrated decision support to assist clinicians.
Clinical guidelines are prevalent but frequently not used. Computer reminder systems can improve adherence to guidelines but have not been widely adopted. We present a computer-based decision support system that combines these elements: 1) pediatric preventive care guidelines encoded in Arden Syntax; 2) a dynamic, scannable paper user interface; and 3) a HL7-compliant interface to existing electronic medical record systems. The result is a system that both delivers "just in time" patient-relevant guidelines to physicians during the clinical encounter and accurately captures structured data from all who interact with the system. The system performs these tasks while remaining sensitive to the workflow constraints of a busy outpatient pediatric practice.
A major obstacle to sharing computable clinical knowledge is the lack of a common language for specifying expressions and criteria. Such a language could be used to specify decision criteria, formulae, and constraints on data and action. Al-though the Arden Syntax addresses this problem for clinical rules, its generalization to HL7's object-oriented data model is limited. The GELLO Expression language is an object-oriented language used for expressing logical conditions and computations in the GLIF3 (GuideLine Interchange Format, v. 3) guideline modeling language. It has been further developed under the auspices of the HL7 Clinical Decision Support Technical Committee, as a proposed HL7 standard., GELLO is based on the Object Constraint Language (OCL), because it is vendor-independent, object-oriented, and side-effect-free. GELLO expects an object-oriented data model. Although choice of model is arbitrary, standardization is facilitated by ensuring that the data model is compatible with the HL7 Reference Information Model (RIM).
To identify features of clinical decision support systems critical for improving clinical practice.
Systematic review of randomised controlled trials.
Literature searches via Medline, CINAHL, and the Cochrane Controlled Trials Register up to 2003; and searches of reference lists of included studies and relevant reviews.
Studies had to evaluate the ability of decision support systems to improve clinical practice.
Studies were assessed for statistically and clinically significant improvement in clinical practice and for the presence of 15 decision support system features whose importance had been repeatedly suggested in the literature.
Seventy studies were included. Decision support systems significantly improved clinical practice in 68% of trials. Univariate analyses revealed that, for five of the system features, interventions possessing the feature were significantly more likely to improve clinical practice than interventions lacking the feature. Multiple logistic regression analysis identified four features as independent predictors of improved clinical practice: automatic provision of decision support as part of clinician workflow (P < 0.00001), provision of recommendations rather than just assessments (P = 0.0187), provision of decision support at the time and location of decision making (P = 0.0263), and computer based decision support (P = 0.0294). Of 32 systems possessing all four features, 30 (94%) significantly improved clinical practice. Furthermore, direct experimental justification was found for providing periodic performance feedback, sharing recommendations with patients, and requesting documentation of reasons for not following recommendations.
Several features were closely correlated with decision support systems' ability to improve patient care significantly. Clinicians and other stakeholders should implement clinical decision support systems that incorporate these features whenever feasible and appropriate.
In this paper, we develop a four-phase model for evaluating architectures for clinical decision support that focuses on: defining a set of desirable features for a decision support architecture; building a proof-of-concept prototype; demonstrating that the architecture is useful by showing that it can be integrated with existing decision support systems and comparing its coverage to that of other architectures. We apply this framework to several well-known decision support architectures, including Arden Syntax, GLIF, SEBASTIAN, and SAGE.
A fuzzy set is a class of objects with a continuum of grades of membership. Such a set is characterized by a membership (characteristic) function which assigns to each object a grade of membership ranging between zero and one. The notions of inclusion, union, intersection, complement, relation, convexity, etc., are extended to such sets, and various properties of these notions in the context of fuzzy sets are established. In particular, a separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
We discuss a reminder system that automatically interprets the results of routine biochemical tests for thyroid hormones. We measured serum levels of thyroid stimulating hormone(TSH), total thyroxin (TT4), triiodothyronine (TT3), thyroxin-binding globulin (TBG), and thyrotropin-releasing hormone (TRH)-stimulated TSH (sTSH [1,2]). The developed knowledge base was used to compare and rate these results with a defined set of diagnoses. Various forms of altered thyroid and/or pituitary function [3-8] were considered. For a given combination of test results, the reminder system classified the entered diagnoses as "obligatory", "possible", or "excluded". The knowledge base is now part of a medical documentation system at the outpatient thyroid department of the Division of Endocrinology and Metabolism of the Vienna General Hospital. We report an evaluation performed after six months of operation. Although further improvement will be required, the knowledge-based reminder system in its present form is capable of improving the quality of manually entered diagnoses.
HEPAXPERT is a knowledge-based system that interprets the results of routine serologic tests for infection with hepatitis A and B viruses. The following tests are included: hepatitis A virus anti-bodies (anti-HAV), IgM antibodies to the hepatitis A virus (IgM anti-HAV), hepatitis A virus (HAV in stool, hepatitis B surface antigen (HBsAg) and antibodies (qualitative anti-HBs, quantitative anti-HBs titre), antibodies to hepatitis B core antigen (anti-HBc and IgM anti-HBc), and hepatitis B envelope antigen (HBeAg) and antibodies (anti-HBe). HEPAXPERT/WWW--an implementation of HEPAXPERT-III for WWW--can be reached by URL http://www.swun.com/hepax of the World Wide Web. After selecting HEPAXPERT/WWW, serologic test results can be entered and will be transferred as an e-mail message for subsequent interpretation which is done off-line with HEPAXPERT-III. The textual interpretation is sent back via e-mail. Each qualitative test for hepatitis A and B antibodies and antigens may produce one of four possible results: positive, negative, borderline, and not tested. To cover the resulting 64 (A) and 57344 (B) combinations of findings, the knowledge base of HEPAXPERT/WWW contains 16 rules for hepatitis A and 131 rules for hepatitis B serology interpretation. This basic knowledge is structured such that all possible combinations of findings can be interpreted and there is no overlap in the premises underlying the rules. The reports that the system automatically generates include: (a) the transferred results of the tests; (b) a detailed analysis of the results, including virus exposure, immunity, stage of illness, prognosis, infectiousness, and vaccination recommendation; and (c) optional: an ID to distinguish the origin of the interpretation requests.
Objective:
The programming language Arden Syntax has been optimised for use in clinical decision support systems. We describe an extension of this language named Fuzzy Arden Syntax, whose original version was introduced in S. Tiffe's dissertation on "Fuzzy Arden Syntax: Representation and Interpretation of Vague Medical Knowledge by Fuzzified Arden Syntax" (Vienna University of Technology, 2003). The primary aim is to provide an easy means of processing vague or uncertain data, which frequently appears in medicine.
Methods:
For both propositional and number data types, fuzzy equivalents have been added to Arden Syntax. The Boolean data type was generalised to represent any truth degree between the two extremes 0 (falsity) and 1 (truth); fuzzy data types were introduced to represent fuzzy sets. The operations on truth values and real numbers were generalised accordingly. As the conditions to decide whether a certain programme unit is executed or not may be indeterminate, a Fuzzy Arden Syntax programme may split. The data in the different branches may be optionally aggregated subsequently.
Results:
Fuzzy Arden Syntax offers the possibility to formulate conveniently Medical Logic Modules (MLMs) based on the principle of a continuously graded applicability of statements. Furthermore, ad hoc decisions about sharp value boundaries can be avoided. As an illustrative example shows, an MLM making use of the features of Fuzzy Arden Syntax is not significantly more complex than its Arden Syntax equivalent; in the ideal case, a programme handling crisp data remains practically unchanged when compared to its fuzzified version. In the latter case, the output data, which can be a set of weighted alternatives, typically depends continuously from the input data.
Conclusion:
In typical applications an Arden Syntax MLM can produce a different output after only slight changes of the input; discontinuities are in fact unavoidable when the input varies continuously but the output is taken from a discrete set of possibilities. This inconvenience can, however, be attenuated by means of certain mechanisms on which the programme flow under Fuzzy Arden Syntax is based. To write a programme making use of these possibilities is not significantly more difficult than to write a programme according to the usual practice.
The Arden Syntax for Medical Logic Modules is a language for encoding medical knowledge bases that consist of independent modules. The Arden Syntax has been used to generate clinical alerts, diagnostic interpretations, management messages, and screening for research studies and quality assurance. An Arden Syntax knowledge base consists of rules called Medical Logic Modules (MLMs), which are stored as simple ASCII files that can be written on any text editor. An MLM is made of slots grouped into three categories: maintenance information, library information, and the actual medical knowledge. Most MLMs are triggered by clinical events, evaluate medical criteria, and, if appropriate, perform an action such as sending a message to a health care provider. This paper provides a detailed tutorial on how to write MLMs.
Primary infection with Toxoplasma gondii, a parasite found in most regions of the world, is asymptomatic in more than 80% of cases. However, primary infection with Toxoplasma gondii in a pregnant woman might cause fetal infection and severe damage. Most cases do not require treatment. This applies to women without any infection (denoted as seronegative) and women who have acquired the infection before conception (denoted as latent). In contrast, women with postconceptual infection require immediate treatment to prevent or ameliorate fetal infection. We have developed an expert system, called Toxoport-I, designed for routine laboratory work, which automatically interprets serological test results of toxoplasma infection. By using the system the clinician can also examine questionable cases by interactively exploring possible results. We used a popular method of designing expert systems applied to medical interpretation and therapy advice, the rule-based one. In order to meet the requirements of automatic interpretation in toxoplasma serology the following characteristics were introduced: the interpretation of sequences of test results, the possibility of excluding inconsistent test results and the adaptability of the knowledge base. A decision graph that covers the different kinds of infections as well as therapy and recommendations for further tests was designed, implemented and was clinically tested by carrying out a retrospective study including 1000 pregnant women. A comparison of Toxoport-I and the clinician's interpretations yielded sensitivity and specificity rates of over 99% each.
A computer assisted documentation of signs and findings in rheumatic diseases is described. This documentation was developed by the Austrian Society for Rheumatology and thought to be a minimal standard for the use by general practitioners. In addition, a knowledge-based basic differential diagnosis support was developed, which differentiates between major groups of rheumatic diseases as inflammatory spine diseases, mechanical or metabolic reasons for spine disorders, inflammatory joint diseases, degenerative or metabolic joint diseases, soft tissue diseases. This presentation describes the results of an evaluation of 75 typical case histories and a second study where 252 case histories were documented retrospectively in this new system. The results of the first showed a pretty good discrimination between the described groups of different diagnoses (sensitivity between 71 and 100 percent for all groups with the exception of metabolic joint diseases, specificity between 75 and 94 percent). The second--retrospective--documentation and diagnostic support showed much weaker results (sensitivity for major groups 74-76 percent). The reasons for the different outcomes are discussed: On the one hand, signs and symptoms from case reports could not be transferred completely in the new documentation, as some findings retrospectively could not be defined sharp enough. On the other hand the study showed, that the sensitivity of well defined disorders as inflammatory joint diseases (exp. rheumatoid arthritis) reaches almost 100 percent, whereas it is as low as 50 percent in some other diseases (e.g. gout) whose characteristic findings and symptoms are suppressed by treatment (drug medication) in many cases. The results show that computer based documentation of rheumatic diseases facilitates the systematized and standardised documentation of patient data. However, a few modifications of the knowledge base as well as the knowledge representation formalisms are necessary to achieve a better performance in differential diagnostic support.
The Arden Syntax was introduced more than 10 years ago, but it is still not in widespread use. One reason might be that for each particular architecture and information system, a different Arden Syntax compiler must be written as well as a program for the runtime execution of the medical logic modules (MLMs). The authors have designed and implemented an architecture that increases the portability of Arden Syntax rules, using the Java platform. The portability to a target information system is achieved by the addition of appropriate adapter components, which they call mappers. These mappers are dynamically selected using explicit and implicit elements of MLMs. Furthermore, they can help translate data from the clinical information system representation into the representation needed by an MLM. This was validated by an experiment in two clinical units. Also, the authors propose a convention to name signals that trigger other MLMs (called intermediate states) so that they remain unique to each institution. The authors implemented this architecture in their clinical system and in an XML-based medical record application that has been used experimentally in their urology and nephrology departments. The Tetrasys company that provided the medical record was able to incorporate their runtime without modifications, and typical MLM execution time was less than 1 sec.
The creation of a database intended for the comparative analysis of the rates of hospital-acquired infections in the 15 countries of the European Union is among the objectives of the HELICS network (Hospitals in Europe Link for Infection Control through Surveillance).
A gap exists between the information contained in published clinical practice guidelines and the knowledge and information that are necessary to implement them. This work describes a process to systematize and make explicit the translation of document-based knowledge into workflow-integrated clinical decision support systems.
This approach uses the Guideline Elements Model (GEM) to represent the guideline knowledge. Implementation requires a number of steps to translate the knowledge contained in guideline text into a computable format and to integrate the information into clinical workflow. The steps include: (1) selection of a guideline and specific recommendations for implementation, (2) markup of the guideline text, (3) atomization, (4) deabstraction and (5) disambiguation of recommendation concepts, (6) verification of rule set completeness, (7) addition of explanations, (8) building executable statements, (9) specification of origins of decision variables and insertions of recommended actions, (10) definition of action types and selection of associated beneficial services, (11) choice of interface components, and (12) creation of requirement specification.
The authors illustrate these component processes using examples drawn from recent experience translating recommendations from the National Heart, Lung, and Blood Institute's guideline on management of chronic asthma into a workflow-integrated decision support system that operates within the Logician electronic health record system.
Using the guideline document as a knowledge source promotes authentic translation of domain knowledge and reduces the overall complexity of the implementation task. From this framework, we believe that a better understanding of activities involved in guideline implementation will emerge.
Commercial drug information systems follow a variety of naming conventions. A smooth electronic exchange of the information in these systems - not only between organizations but even within a single organization - is crucial in assuring patient safety. This exchange requires a standardized nomenclature. To meet this need, the National Library of Medicine (NLM) created RxNorm, a standardized nomenclature for clinical drugs that is one of a suite of standards designated for use in US federal government systems for the electronic exchange of clinical health information.
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