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Publications (162)
BACKGROUND
Patients with melanoma receiving immunotherapy with immune-checkpoint inhibitors (ICIs) often experience immune-related adverse events (AEs), cancer-related fatigue and emotional distress, affecting health-related quality of life (HRQoL) and clinical outcome to immunotherapy. EHealth tools can aid cancer patients in addressing issues suc...
The integration of information from now widely available -omics and imaging modalities at multiple time and spatial scales with personal health records has become the standard of disease care in modern public health. Moreover, given the ever-increasing role of the World Wide Web as a source of information in many domains including healthcare, acces...
Background
Since treatment with immune checkpoint inhibitors (ICIs) is becoming standard therapy for patients with high-risk and advanced melanoma, an increasing number of patients experience treatment-related adverse events such as fatigue. Until now, studies have demonstrated the benefits of using eHealth tools to provide either symptom monitorin...
Background:
Within the CAPABLE project the authors developed a multi-agent system that relies on a distributed architecture. The system provides cancer patients with coaching advice and supports their clinicians with suitable decisions based on clinical guidelines.
Objectives:
As in many multi-agent systems we needed to coordinate the activities...
BACKGROUND
Since treatment with immune-checkpoint inhibitors (ICIs) is becoming standard therapy for high-risk and advanced-melanoma patients, an increasing number of patients experience treatment-related adverse events such as fatigue. Until now, studies have demonstrated the benefits of using eHealth tools in providing either symptom monitoring o...
Objective:
The study has dual objectives. Our first objective (1) is to develop a community-of-practice-based evaluation methodology for knowledge-intensive computational methods. We target a whitebox analysis of the computational methods to gain insight on their functional features and inner workings. In more detail, we aim to answer evaluation q...
We are developing a virtual coaching system that helps patients adhere to behavior change interventions (BCI). Our proposed system predicts whether a patient will perform the targeted behavior and uses counterfactual examples with feature control to guide personalizsation of BCI. We evaluated our prediction model using simulated patient data with v...
Clinical practice guidelines (CPGs) are patient management tools that synthesize medical knowledge into an actionable format. CPGs are disease specific with limited applicability to the management of complex patients suffering from multimorbidity. For the management of these patients, CPGs need to be augmented with secondary medical knowledge comin...
This report presents the agenda and major outcome of the 7th International Workshop on Health Intelligence (W3PHIAI 2023), which will be held in conjunction with the 37th AAAI Conference on Artificial Intelligence on FEB 13 - 14, 2023 in Washington, DC, USA.
Increasingly complex learning methods such as boosting, bagging and deep learning have made ML models more accurate, but harder to interpret and explain, culminating in black-box machine learning models. Model developers and users alike are often presented with a trade-off between performance and intelligibility, especially in high-stakes applicati...
Designing effective theory-driven digital behaviour change interventions (DBCI) is a challenging task. To ease the design process, and assist with knowledge sharing and evaluation of the DBCI, we propose the SATO (IDEAS expAnded wiTh BCIO) design workflow based on the IDEAS (Integrate, Design, Assess, and Share) framework and aligned with the Behav...
Automated machine learning (AutoML) has made life easier for data analysts or scientists by providing quick insights into data by building machine learning (ML) models. AutoML techniques are applied to vast areas from image processing, speech recognition, natural language processing reinforcement learning, and more. However, there is still room for...
Treatment of patients with multimorbidity is one of the greatest challenges for clinical decision support. While evidence-based management of specific diseases is supported by clinical practice guidelines, concurrent application of multiple guidelines requires checking for possible adverse interactions between interventions and mitigating them, bef...
Designing theory-driven digital health interventions is a challenging task that needs support. We created a guide for the incomers in the field on how to design digital health interventions with case studies from the Cancer Better Life Experience (CAPABLE) European project. The guide explains how behaviour change theories can inform customisation a...
Current research on imbalanced data recognises that class imbalance is aggravated by other data intrinsic characteristics, among which class overlap stands out as one of the most harmful. The combination of these two problems creates a new and difficult scenario for classification tasks and has been discussed in several research works over the past...
We conducted a pilot intervention on a healthy population to evaluate the impact of wellbeing activities included in the CAPABLE patient application to understand factors impacting engagement with the intervention.
Multimorbidity, the coexistence of two or more health conditions, has become more prevalent as mortality rates in many countries have declined and their populations have aged. Multimorbidity presents significant difficulties for Clinical Decision Support Systems (CDSS), particularly in cases where recommendations from relevant clinical guidelines o...
Adherence to therapy is one of the major determinants of therapy success, while non-adherence leads to worsening of patient condition and increased healthcare costs. The aim of our work is to evaluate therapies recommended by a clinical practice guideline in order to select a therapy that is most suited for a patient’s adherence profile and account...
Increasingly complex learning methods such as boosting, bagging and deep learning have made ML models more accurate, but harder to understand and interpret. A tradeoff between performance and intelligibility is often to be faced, especially in high-stakes applications like medicine. In the present article we propose a novel methodological approach...
The adoption of the advanced data analytics methods has been limited in industries governed by strict data reuse regulations, such as healthcare. Barriers to data access and sharing have affected numerous research and development initiatives in healthcare resulting in major delays, extensive use of resources for data access and findings originating...
We propose a methodological framework to support the development of personalized courses that improve patients’ understanding of their condition and prescribed treatment. Inspired by Intelligent Tutoring Systems (ITSs), the framework uses an eLearning ontology to express domain and learner models and to create a course. We combine the ontology with...
The complexity of patient care is growing due to an ageing population. As chronic illnesses become more common, the incidence of multi-morbidity increases. Generating disease management plans for multi-morbid patients requires the integration of multiple evidence-based interventions, represented as clinical practice guidelines (CPGs), that are desi...
The CAPABLE project aims to improve the wellbeing of cancer patients managed at home via a coaching system recommending personalized evidence-based health behavioral change interventions and supporting patients compliance. Focusing on managing stress via deep breathing intervention, we hypothesise that the patients are more likely to perform sugges...
The CAncer PAtient Better Life Experience (CAPABLE) project combines the most advanced technologies for data and knowledge management with a socio-psychological approach, to develop a coaching system for improving the quality of life of cancer patients managed at home. The team includes complementary expertise in data- and knowledge-driven AI, data...
The CAPABLE project has been funded by the EU Horizon 2020 Programme over the years 2020-24 to support home care. A system is being designed and implemented supporting remote monitoring and virtual coaching for cancer patients. The system is based on a distributed modular architecture involving many components encapsulating various knowledge. The C...
Introduction
Thanks to improvement of care, cancer has become a chronic condition. But due to the toxicity of treatment, the importance of supporting the quality of life (QoL) of cancer patients increases. Monitoring and managing QoL relies on data collected by the patient in his/her home environment, its integration, and its analysis, which suppor...
The paper describes a computer tool dedicated to the comprehensive analysis of lung changes in computed tomography (CT) images. The correlation between the dose delivered during radiotherapy and pulmonary fibrosis is offered as an example analysis. The input data, in DICOM (Digital Imaging and Communications in Medicine) format, is provided from CT...
As the population ages, patients’ complexity and the scope of their care is increasing. Over 60% of the population is 65 years of age or older and suffers from multi-morbidity, which is associated with two times as many patient-physician encounters. Yet clinical practice guidelines (CPGs) are developed to treat a single disease. To reconcile these...
In healthcare domains, dealing with missing data is crucial since absent observations compromise the reliability of decision support models. K-nearest neighbours imputation has proven beneficial since it takes advantage of the similarity between patients to replace missing values. Nevertheless, its performance largely depends on the distance functi...
Introduction - Thanks to improvement of care, cancer has become a chronic condition. But due to the toxicity of treatment, the importance of supporting the quality of life (QoL) of cancer patients increases. Monitoring and managing QoL relies on data collected by the patient in his/her home environment, its integration, and its analysis, which supp...
When deciding about surgical treatment options, an important aspect of the decision-making process is the potential risk of complications. A risk assessment performed by a spinal surgeon is based on their knowledge of the best available evidence and on their own clinical experience. The objective of this work is to demonstrate the differences in th...
The 2020 International Workshop on Health Intelligences is held in conjunction with AAAI-20 on Feb 7, 2020, in New York City, NY USA.
Background and purpose:
Teamwork has become a modus operandi in healthcare and delivery of patient care by an interdisciplinary healthcare team (IHT) is now a prevailing modality of care. We argue that a formal and automated support framework is needed for an IHT to properly leverage information technology resources. Such a framework should allow...
Objectives:
Machine learning models have been used to predict mortality among patients requiring rapid response team activation. The goal of our study was to assess the impact of adding laboratory values into the model.
Design:
A gradient boosted decision tree model was derived and internally validated to predict a primary outcome of in-hospital...
A prominent characteristic of clinical data is their heterogeneity - such data include structured examination records and laboratory results, unstructured clinical notes, raw and tagged images, and genomic data. This heterogeneity poses a formidable challenge while constructing diagnostic and therapeutic decision models that are currently based on...
The third AAAI Health Intelligence workshop, held in conjunction with the 33rd AAAI Conference on Artificial Intelligence (AAAI-19) on Jan 27 - Feb 1, 2019, at the Hilton Hawaiian Village in Honolulu, Hawaii, USA, addresses various aspects of using AI for improving population and personalized healthcare and is structured in two tracks focusing on p...
This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019.
The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulat...
Regardless of potential benefits and better outcomes, adoption of shared decision-making between a patient and providers involved in his/her care is still in its infancy. This paper intends to fill this gap by formalizing shared decision-making, situating it as part of team-based care delivery, and incorporating workflow concepts allowing for ident...
Regardless of potential benefits and better outcomes, adoption of shared decision-making between a patient and providers involved in his/her care is still in its infancy. This paper intends to fill this gap by formalizing shared decision-making, situating it as part of team-based care delivery, and incorporating workflow concepts allowing for ident...
Poor patient compliance to therapy results in a worsening condition that often increases healthcare costs. In the MobiGuide project, we developed an evidence-based clinical decision-support system that delivered personalized reminders and recommendations to patients, helping to achieve higher therapy compliance. Yet compliance could still be improv...
Progress in medical imaging and computer vision has enabled us to rely on machines for the detection or diagnosis of many diseases, including eye-related problems. One of them is wet age-related macular degeneration (wet AMD) which is a type of age-related macular degeneration. Wet AMD causes the detachment of retinal pigment epithelium layer (RPE)...
This workshop will address various aspects of using AI for improving population and personalized healthcare and is structured in two tracks focusing on population (W3PHI) and personalized health (HIAI). Following the success of AAAI-W3PHI 2014-16, AAAI-HIAI 2013-16, and the first joint workshop at AAAI-17 (W3PHIAI-17), this workshop aims to bring t...
Interdisciplinary healthcare teams (IHTs) are involved in clinical processes composed of tasks requiring specific capabilities from different disciplines, often executed at different times. Although some hospitals use Business Process Management (BPM) suites to support their clinical activities, these tools are often unable to support the dynamic a...
The AAAI-17 workshop program included 17 workshops covering a wide range of topics in AI. Workshops were held Sunday and Monday, February 4-5, 2017 at the Hilton San Francisco Union Square in San Francisco, California, USA. This report contains summaries of 12 of the workshops, and brief abstracts of the remaining 5.
Clinical data is characterized not only by its constantly increasing volume but also by its diversity. Information collected in clinical information systems such as electronic health records is highly heterogeneous and it includes structured laboratory and examination reports, unstructured clinical notes, images, and more often genetic data. This h...
Computer-interpretable implementations of clinical guidelines (CIGs) add knowledge that is outside the scope of the original guideline. This knowledge can customize CIGs to patients’ psycho-social context or address comorbidities that are common in the local population, potentially increasing standardization of care and patient compliance. We devel...
In this paper we propose a new algorithm called SPIDER3 for selective preprocessing of multi-class imbalanced data sets. While it borrows selected ideas (i.e., combination of relabeling and local resampling) from its predecessor – SPIDER2, it introduces several important extensions. Unlike SPIDER2, it is able to handle directly multi-class problems...
The First Joint AAAI Workshop on Health Intelligence (W3PHIAI 2017) was held in San Francisco, California, on February 4-5, 2017. This workshop follows the success of AAAI-W3PHI 2014 in Québec City, Québec, Canada, AAAI-W3PHI 2015 in Austin, Texas, USA, AAAI-W3PHI 2016 in Phonix, Arizona, USA and HIAI 2013-2016 held in conjunction with the twenty-s...
The First Joint AAAI Workshop on Health Intelligence (W3PHIAI 2017) was held in San Francisco, California, on February 4-5, 2017. This workshop follows the success of AAAI-W3PHI 2014 in Québec City, Québec, Canada, AAAI-W3PHI 2015 in Austin, Texas, USA, AAAI-W3PHI 2016 in Phonix, Arizona, USA and HIAI 2013-2016 held in conjunction with the twenty-s...
In this paper we describe results of an experimental study where we checked the impact of various difficulty factors in imbalanced data sets on the performance of selected classifiers applied alone or combined with several preprocessing methods. In the study we used artificial data sets in order to systematically check factors such as dimensionalit...
In this work we propose a comprehensive framework based on first-order logic (FOL) for mitigating (identifying and addressing) interactions between multiple clinical practice guidelines (CPGs) applied to a multi-morbid patient while also considering patient preferences related to the prescribed treatment. With this framework we respond to two funda...
Background:
A significant challenge associated with practicing evidence-based medicine is to provide physicians with relevant clinical information when it is needed. At the same time it appears that the notion of relevance is subjective and its perception is affected by a number of contextual factors.
Objectives:
To assess to what extent physici...
In this paper we describe an experimental study where we analyzed data difficulty factors encountered in imbalanced clinical data sets and examined how selected data preprocessing methods were able to address these factors. We considered five data sets describing various pediatric acute conditions. In all these data sets the minority class was spar...
In healthcare organizations, clinical workflows are executed by interdisciplinary healthcare teams (IHTs) that operate in ways that are difficult to manage. Responding to a need to support such teams, we designed and developed the MET4 multi-agent system that allows IHTs to manage patients according to presentation-specific clinical workflows. In t...
The increasing prevalence of multimorbidity is a challenge for physicians who have to manage a constantly growing number of patients with simultaneous diseases. Adding to this challenge is the need to incorporate patient preferences as key components of the care process, thanks in part to the emergence of personalized and participatory medicine. In...
The increasing prevalence of multimorbidity is a challenge for physicians who have to manage a constantly growing number of patients with simultaneous diseases. Adding to this challenge is the need to incorporate patient preferences as key components of the care process, thanks in part to the emergence of personalized and participatory medicine. In...
We propose a novel approach to the problem of the classification with test costs understood as costs of obtaining attribute values of classified examples. Many existing approaches construct classifiers in order to control the tradeoff between test costs and the prediction accuracy (or misclassification costs). The aim of the proposed method is to r...
The use of business workflow models in healthcare is limited because of insufficient capture of complexities associated with behavior of interdisciplinary healthcare teams that execute healthcare workflows. In this paper we present a novel framework that builds on the well-founded business workflow model formalism and related infrastructures and in...
In the paper we describe a sequential classification scheme that iteratively explores levels of abstraction in the description of examples. These levels of abstraction represent attribute values of increasing precision. Specifically, we assume attribute values constitute an ontology (i.e., attribute value ontology) reflecting a domain-specific back...
Clinical practice guidelines (CPGs) implement evidence-based medicine designed to help generate a therapy for a patient suffering from a single disease. When applied to a comorbid patient, the concurrent combination of treatment steps from multiple CPGs is susceptible to adverse interactions in the resulting combined therapy (i.e., a therapy establ...
Participatory medicine refers to the equal participation of patients and interdisciplinary healthcare team (IHT) members as part of care delivery. Facilitating workflow execution is a significant challenge for participatory medicine because of the need to integrate IHT members into a common workflow. A further challenge is that patient preferences...
This paper describes MET4, a multi-agent system that supports interdisciplinary healthcare teams (IHTs) in executing patient care workflows. Using the concept of capability, the system facilitates the maintenance of an IHT and assignment of workflow tasks to the most appropriate team members. Moreover, following the principles of participatory medi...
Clinical practice guidelines (CPGs) were originally designed to help with evidence-based management of a single disease and such a single disease focus has impacted research on CPG computerization. This computerization is mostly concerned with supporting different representation formats and identifying potential inconsistencies in the definitions o...
Background:
Online medical knowledge repositories such as MEDLINE and The Cochrane Library are increasingly used by physicians to retrieve articles to aid with clinical decision making. The prevailing approach for organizing retrieved articles is in the form of a rank-ordered list, with the assumption that the higher an article is presented on a l...