Figure 8 - uploaded by Samar Helou
Content may be subject to copyright.
Generation of the health summary composition (see online version for colours)

Generation of the health summary composition (see online version for colours)

Context in source publication

Context 1
... we generated the EHR compositions with unique IDs. Then each generation plan was applied to create different branches of the EHR record as shown in Figures 8, 9, 10 and 11. The generation plans contained possible value lists that were randomly assigned in a uniform way across all the generated instances. ...

Similar publications

Article
Full-text available
Background: Anti-TNF-alpha (anti-TNFα) therapies have transformed the care and management of inflammatory bowel disease (IBD). However, they are expensive and ineffective in greater than 50% of patients, and they increase the risk of infections, liver issues, arthritis, and lymphoma. With 1.6 million Americans suffering from IBD and global prevale...
Preprint
Full-text available
In this paper, we propose a pipeline to convert grade school level algebraic word problem into program of a formal languageA-IMP. Using natural language processing tools, we break the problem into sentence fragments which can then be reduced to functions. The functions are categorized by the head verb of the sentence and its structure, as defined b...
Article
Full-text available
To develop commonsense-grounded NLP applications, a comprehensive and accurate commonsense knowledge graph (CKG) is needed. It is time-consuming to manually construct CKGs and many research efforts have been devoted to the automatic construction of CKGs. Previous approaches focus on generating concepts that have direct and obvious relationships wit...

Citations

... OpenEHR-related research is gradually becoming one of the most discussed semantic interoperability-related research topics. Such research involves archetype modeling [17][18][19][20][21][22][23], data persistence [24][25][26], language design [27], model mapping [28], model retrieval [29,30], and reuse [19]. ...
Article
Background The semantic interoperability of health care information has been a critical challenge in medical informatics and has influenced the integration, sharing, analysis, and use of medical big data. International standard organizations have developed standards, approaches, and models to improve and implement semantic interoperability. The openEHR approach—one of the standout semantic interoperability approaches—has been implemented worldwide to improve semantic interoperability based on reused archetypes. Objective This study aimed to verify the feasibility of implementing semantic interoperability in different countries by comparing the openEHR-based information models of 2 acute coronary syndrome (ACS) registries from China and New Zealand. Methods A semantic archetype comparison method was proposed to determine the semantics reuse degree of reused archetypes in 2 ACS-related clinical registries from 2 countries. This method involved (1) determining the scope of reused archetypes; (2) identifying corresponding data items within corresponding archetypes; (3) comparing the semantics of corresponding data items; and (4) calculating the number of mappings in corresponding data items and analyzing results. Results Among the related archetypes in the two ACS-related, openEHR-based clinical registries from China and New Zealand, there were 8 pairs of reusable archetypes, which included 89 pairs of corresponding data items and 120 noncorresponding data items. Of the 89 corresponding data item pairs, 87 pairs (98%) were mappable and therefore supported semantic interoperability, and 71 pairs (80%) were labeled as “direct mapping” data items. Of the 120 noncorresponding data items, 114 (95%) data items were generated via archetype evolution, and 6 (5%) data items were generated via archetype localization. Conclusions The results of the semantic comparison between the two ACS-related clinical registries prove the feasibility of establishing the semantic interoperability of health care data from different countries based on the openEHR approach. Archetype reuse provides data on the degree to which semantic interoperability exists when using the openEHR approach. Although the openEHR community has effectively promoted archetype reuse and semantic interoperability by providing archetype modeling methods, tools, model repositories, and archetype design patterns, the uncontrolled evolution of archetypes and inconsistent localization have resulted in major challenges for achieving higher levels of semantic interoperability.
... There are several criteria, such as data model, performance, data persistence, and CAP support [19], which must be considered when choosing which NoSQL store to be used. Various data modeling approaches [3,[20][21][22][23][24][25][26][27] have been introduced for medical data persistence according to use case scenarios. These works investigate not only the type of NoSQL store that has to be chosen but which NoSQL products in that type will be used [19]. ...
... Graphs have been used to quickly connect the different types of related data that have produced high-density graphs. Such a vast collection of graphs caused by the above sources with billions of vertices and edges (relationships) made the graphs prevalent, leading to the challenges and opportunities of analysing and interpreting the graph data (El Helou et al., 2019). ...
... For such clinical practice, healthcare providers typically perform create, read, update, and destroy (CRUD) operations to retrieve and modify a relatively small number of several EHR extracts easily. Minimizing the response time of these CRUD operations may enhance EHRs' usability and functionality [31]. A fundamental principle in medical systems is that clinical data cannot be overwritten. ...
... Graph databases have been recently introduced as a potential alternative to relational databases for handling graph-like data structures [73,74]. A graph-based implementation method [31] was suggested and evaluated for an archetype-oriented repository utilizing a labeled property graph database. This method was used as an alternative to traditional relational database architecture for clinical data storage. ...
... As the RM includes several classes in a deep tree hierarchy, it has a graph-like architecture. As a result, mapping it to a graph model and storing it in a graph database would be easy [31]. ...
Article
With the extensive adoption of electronic health records (EHRs) by several healthcare organizations, more efforts are needed to manage and utilize such massive, various, and complex healthcare data. Databases' performance and suitability to health care tasks are dramatically affected by how their data storage model and query capabilities are well-adapted to the use case scenario. On the other hand, standardized healthcare data modeling is one of the most favorable paths for achieving semantic interoperability, facilitating patient data integration from different healthcare systems. This paper compares the state-of-the-art of the most crucial database management systems used for storing standardized EHRs data. It discusses different database models' appropriateness for meeting different EHRs functions with different database specifications and workload scenarios. Insights into relevant literature show how flexible NoSQL databases (document, column, and graph) effectively deal with standardized EHRs data's distinctive features, especially in the distributed healthcare system, leading to better EHR.
... To ensure the success of our approach, the systems needs to be initially built in a way that makes their adaptation simple. To do so, the designers can follow existing frameworks that allow them to develop adaptable software architectures [23]- [25]. After these systems are implemented and used, the designers can follow our approach to understand which features to redesign. ...
Article
Full-text available
Electronic Medical Record (EMR) systems are the computers used inside healthcare clinics. EMR systems have multiple stakeholders whose needs continuously evolve. The traditional EMR design approach focuses on designing systems that perfectly fit the requirements of some stakeholders as they are understood in the initial design stages. This results in EMR systems that do not answer all the stakeholder's needs and quickly become outdated. To address the limitations of the traditional EMR design approach, we propose a utilitarian redesign approach for EMR systems. By "utilitarian redesign", we mean that the designers continuously redesign the EMR system with the aim of maximizing the satisfaction of all the stakeholders. Our approach allows the designers to (i) identify the features to redesign and (ii) to know which features would bring the largest good to the largest number of stakeholders. We showcase the approach using a case study of redesigning an EMR system in Japanese antenatal care settings. We also evaluate our approach with 21 participants split over 7 workshops. Our results showed that the approach provides useful information to help the designers make utilitarian redesign choices. Even though our approach was applied to EMR systems, it may also be applied to redesign other complex socio-technical systems and potentially maximize the good for the largest number of stakeholders.
Preprint
Full-text available
Graph representation learning is a method for introducing how to effectively construct and learn patient embeddings using electronic medical records. Adapting the integration will support and advance the previous methods to predict the prognosis of patients in network models. This study aims to address the challenge of implementing complex and highly heterogeneous dataset, including the following: (1) demonstrating how to build a multi-attributed and multi-relational graph model (2) and applying a downstream disease prediction task of patient’s prognosis using HinSAGE algorithm. We present a bipartite graph schema and a graph database construction in detail. The first constructed graph database illustrates a query of a predictive network which provides analytical insights using graph representation of a patient’s journey. Moreover, we demonstrate an alternative bipartite model where we apply the model to the HinSAGE to perform the link prediction task for predicting the event occurrence. Consequently, the performance evaluation indicated that our heterogeneous graph model successfully predicted as baseline models. Overall, our graph database successfully demonstrated efficient real-time query performance and showed HinSAGE implementation to predict cardiovascular diseases event outcomes on supervised link prediction learning.
Article
Background The widespread adoption of electronic health records (EHRs) has facilitated the secondary use of EHR data for clinical research. However, screening eligible patients from EHRs is a challenging task. The concepts in eligibility criteria are not completely matched with EHRs, especially derived concepts. The lack of high-level expression of Structured Query Language (SQL) makes it difficult and time consuming to express them. The openEHR Expression Language (EL) as a domain-specific language based on clinical information models shows promise to represent complex eligibility criteria. Objective The study aims to develop a patient-screening tool based on EHRs for clinical research using openEHR to solve concept mismatch and improve query performance. Methods A patient-screening tool based on EHRs using openEHR was proposed. It uses the advantages of information models and EL in openEHR to provide high-level expressions and improve query performance. First, openEHR archetypes and templates were chosen to define concepts called simple concepts directly from EHRs. Second, openEHR EL was used to generate derived concepts by combining simple concepts and constraints. Third, a hierarchical index corresponding to archetypes in Elasticsearch (ES) was generated to improve query performance for subqueries and join queries related to the derived concepts. Finally, we realized a patient-screening tool for clinical research. Results In total, 500 sentences randomly selected from 4691 eligibility criteria in 389 clinical trials on stroke from the Chinese Clinical Trial Registry (ChiCTR) were evaluated. An openEHR-based clinical data repository (CDR) in a grade A tertiary hospital in China was considered as an experimental environment. Based on these, 589 medical concepts were found in the 500 sentences. Of them, 513 (87.1%) concepts could be represented, while the others could not be, because of a lack of information models and coarse-grained requirements. In addition, our case study on 6 queries demonstrated that our tool shows better query performance among 4 cases (66.67%). Conclusions We developed a patient-screening tool using openEHR. It not only helps solve concept mismatch but also improves query performance to reduce the burden on researchers. In addition, we demonstrated a promising solution for secondary use of EHR data using openEHR, which can be referenced by other researchers.