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Domínguez J, Prociuk D, Marović B, Čyras K, Cocarascu O, Ruiz F, Mi E, Mi E, Ramtale C, Rago A, Darzi A, Toni F, Curcin V, Delaney B. ROAD2H: Development and evaluation of an open-source explainable artificial intelligence approach for managing co-morbidity and clinical guidelines. Learn Health Syst 2024; 8:e10391. [PMID: 38633019 PMCID: PMC11019374 DOI: 10.1002/lrh2.10391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 07/29/2023] [Accepted: 08/07/2023] [Indexed: 04/19/2024] Open
Abstract
Introduction Clinical decision support (CDS) systems (CDSSs) that integrate clinical guidelines need to reflect real-world co-morbidity. In patient-specific clinical contexts, transparent recommendations that allow for contraindications and other conflicts arising from co-morbidity are a requirement. In this work, we develop and evaluate a non-proprietary, standards-based approach to the deployment of computable guidelines with explainable argumentation, integrated with a commercial electronic health record (EHR) system in Serbia, a middle-income country in West Balkans. Methods We used an ontological framework, the Transition-based Medical Recommendation (TMR) model, to represent, and reason about, guideline concepts, and chose the 2017 International global initiative for chronic obstructive lung disease (GOLD) guideline and a Serbian hospital as the deployment and evaluation site, respectively. To mitigate potential guideline conflicts, we used a TMR-based implementation of the Assumptions-Based Argumentation framework extended with preferences and Goals (ABA+G). Remote EHR integration of computable guidelines was via a microservice architecture based on HL7 FHIR and CDS Hooks. A prototype integration was developed to manage chronic obstructive pulmonary disease (COPD) with comorbid cardiovascular or chronic kidney diseases, and a mixed-methods evaluation was conducted with 20 simulated cases and five pulmonologists. Results Pulmonologists agreed 97% of the time with the GOLD-based COPD symptom severity assessment assigned to each patient by the CDSS, and 98% of the time with one of the proposed COPD care plans. Comments were favourable on the principles of explainable argumentation; inclusion of additional co-morbidities was suggested in the future along with customisation of the level of explanation with expertise. Conclusion An ontological model provided a flexible means of providing argumentation and explainable artificial intelligence for a long-term condition. Extension to other guidelines and multiple co-morbidities is needed to test the approach further.
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Affiliation(s)
- Jesús Domínguez
- Department of Population Health SciencesKing's College LondonLondonUK
| | | | | | | | | | - Francis Ruiz
- London School of Hygiene and Tropical MedicineLondonUK
| | - Ella Mi
- University of OxfordOxfordUK
| | - Emma Mi
- University of OxfordOxfordUK
| | | | | | | | | | - Vasa Curcin
- Department of Population Health SciencesKing's College LondonLondonUK
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Bottrighi A, Piovesan L, Terenziani P. Supporting physicians in the coordination of distributed execution of CIGs to treat comorbid patients. Artif Intell Med 2023; 135:102472. [PMID: 36628779 DOI: 10.1016/j.artmed.2022.102472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 11/25/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022]
Abstract
Clinical Practice Guidelines (CPGs) encode the "best" medical practices to treat patients affected by a specific disease and are widely used in the medical practice. Starting from the '90s', several Computer-Interpretable Guideline (CIG) systems have been devised to provide physicians with CPG-based decision support. CPGs (and CIGs) are devoted to provide evidence-based recommendations for one specific disease. In order to support the treatment of patients affected by multiple diseases (i.e., comorbid patients), challenging additional tasks have to be addressed, such as (i) the detection of the interactions between CIG actions, (ii) their management, and, finally, (iii) the "merge" or conciliation of the CIGs. Several CIG approaches have been recently extended in order to face (at least one of) such challenging problems, and one of them is GLARE. However, besides the solutions to tasks (i)-(iii) above, the "run-time" support to physicians treating a comorbid patient requires additional capabilities, to support the distribution of the management of interactions and of the execution of CIGs among different physicians. In this paper, we propose a general framework, based on GLARE and GLARE-SSCPM, to provide such additional capabilities.
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Affiliation(s)
- Alessio Bottrighi
- DISIT, Università del Piemonte Orientale, Viale Teresa Michel 11, Alessandria, Italy; AI@UPO, Università del Piemonte Orientale, Vercelli, Italy.
| | - Luca Piovesan
- DISIT, Università del Piemonte Orientale, Viale Teresa Michel 11, Alessandria, Italy; AI@UPO, Università del Piemonte Orientale, Vercelli, Italy.
| | - Paolo Terenziani
- DISIT, Università del Piemonte Orientale, Viale Teresa Michel 11, Alessandria, Italy; AI@UPO, Università del Piemonte Orientale, Vercelli, Italy.
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Litchfield I, Turner AM, Ferreira Filho JB, Lee M, Weber P. Automated conflict resolution for patients with multiple morbidity being treated using more than one set of single condition clinical guidance: A case study. Comput Biol Med 2022; 144:105381. [DOI: 10.1016/j.compbiomed.2022.105381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 02/14/2022] [Accepted: 03/02/2022] [Indexed: 11/15/2022]
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Mouazer A, Tsopra R, Sedki K, Letord C, Lamy JB. Decision-support systems for managing polypharmacy in the elderly: A scoping review. J Biomed Inform 2022; 130:104074. [PMID: 35470079 DOI: 10.1016/j.jbi.2022.104074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 04/07/2022] [Accepted: 04/08/2022] [Indexed: 10/18/2022]
Abstract
Polypharmacy, the consuming of more than five drugs, is a public health problem. It can lead to many interactions and adverse drug reactions and is very expensive. Therapeutic guidelines for managing polypharmacy in the elderly have been issued, but are highly complex, limiting their use. Decision-support systems have therefore been developed to automate the execution of these guidelines, or to provide information about drugs adapted to the context of polypharmacy. These systems differ widely in terms of their technical design, knowledge sources and evaluation methods. We present here a scoping review of electronic systems for supporting the management, by healthcare providers, of polypharmacy in elderly patients. Most existing reviews have focused mainly on evaluation results, whereas the present review also describes the technical design of these systems and the methodologies for developing and evaluating them. A systematic bibliographic search identified 19 systems differing considerably in terms of their technical design (rule-based systems, documentary approach, mixed); outputs (textual report, alerts and/or visual approaches); and evaluations (impact on clinical practices, impact on patient outcomes, efficiency and/or user satisfaction). The evaluations performed are minimal (among all the systems identified, only one system has been evaluated according to all the criteria mentioned above) and no machine learning systems and/or conflict management systems were retrieved. This review highlights the need to develop new methodologies, combining various approaches for decision support system in polypharmacy.
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Affiliation(s)
- Abdelmalek Mouazer
- Université Sorbonne Paris Nord, LIMICS, Sorbonne Université, INSERM, F-93000 Bobigny, France.
| | - Rosy Tsopra
- INSERM, Université de Paris, Sorbonne Université, Centre de Recherche des Cordeliers, F-75006 Paris, France; INRIA, HeKA, INRIA Paris, France; Department of Medical Informatics, Hôpital Européen Georges-Pompidou, AP-HP, Paris, France
| | - Karima Sedki
- Université Sorbonne Paris Nord, LIMICS, Sorbonne Université, INSERM, F-93000 Bobigny, France
| | - Catherine Letord
- Université Sorbonne Paris Nord, LIMICS, Sorbonne Université, INSERM, F-93000 Bobigny, France; Department of Biomedical Informatics, Rouen University Hospital, Normandy, France
| | - Jean-Baptiste Lamy
- Université Sorbonne Paris Nord, LIMICS, Sorbonne Université, INSERM, F-93000 Bobigny, France
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Sassoon I, Kökciyan N, Modgil S, Parsons S. Argumentation schemes for clinical decision support. ARGUMENT & COMPUTATION 2021. [DOI: 10.3233/aac-200550] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
This paper demonstrates how argumentation schemes can be used in decision support systems that help clinicians in making treatment decisions. The work builds on the use of computational argumentation, a rigorous approach to reasoning with complex data that places strong emphasis on being able to justify and explain the decisions that are recommended. The main contribution of the paper is to present a novel set of specialised argumentation schemes that can be used in the context of a clinical decision support system to assist in reasoning about what treatments to offer. These schemes provide a mechanism for capturing clinical reasoning in such a way that it can be handled by the formal reasoning mechanisms of formal argumentation. The paper describes how the integration between argumentation schemes and formal argumentation may be carried out, sketches how this is achieved by an implementation that we have created and illustrates the overall process on a small set of case studies.
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Affiliation(s)
- Isabel Sassoon
- Department of Computer Science, Brunel University London, UK. E-mail:
| | - Nadin Kökciyan
- School of Informatics, University of Edinburgh, UK. E-mail:
| | - Sanjay Modgil
- Department of Informatics, King’s College London, UK. E-mail:
| | - Simon Parsons
- Department of Computer Science, University of Lincoln, UK. E-mail:
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Van Woensel W, Abidi SSR, Abidi SR. Decision support for comorbid conditions via execution-time integration of clinical guidelines using transaction-based semantics and temporal planning. Artif Intell Med 2021; 118:102127. [PMID: 34412844 DOI: 10.1016/j.artmed.2021.102127] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Revised: 05/04/2021] [Accepted: 06/22/2021] [Indexed: 11/25/2022]
Abstract
In case of comorbidity, i.e., multiple medical conditions, Clinical Decision Support Systems (CDSS) should issue recommendations based on all relevant disease-related Clinical Practice Guidelines (CPG). However, treatments from multiple comorbid CPG often interact adversely (e.g., drug-drug interactions) or introduce operational inefficiencies (e.g., redundant scans). A common solution is the a-priori integration of computerized CPG, which involves integration decisions such as discarding, replacing or delaying clinical tasks (e.g., treatments) to avoid adverse interactions or inefficiencies. We argue this insufficiently deals with execution-time events: as the patient's health profile evolves, acute conditions occur, and real-time delays take place, new CPG integration decisions will often be needed, and prior ones may need to be reverted or undone. Any realistic CPG integration effort needs to further consider temporal aspects of clinical tasks-these are not only restricted by temporal constraints from CPGs (e.g., sequential relations, task durations) but also by CPG integration efforts (e.g., avoid treatment overlap). This poses a complex execution-time challenge and makes it difficult to determine an up-to-date, optimal comorbid care plan. We present a solution for dynamic integration of CPG in response to evolving health profiles and execution-time events. CPG integration policies are formulated by clinical experts for coping with comorbidity at execution-time, with clearly defined integration semantics that build on Description and Transaction Logics. A dynamic planning approach reconciles temporal constraints of CPG tasks at execution-time based on their importance, and continuously updates an optimal task schedule.
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Affiliation(s)
- William Van Woensel
- NICHE Research Group, Faculty of Computer Science, Dalhousie University, 6050 University Ave, Halifax, NS B3H 1W5, Canada.
| | - Syed Sibte Raza Abidi
- NICHE Research Group, Faculty of Computer Science, Dalhousie University, 6050 University Ave, Halifax, NS B3H 1W5, Canada.
| | - Samina Raza Abidi
- Faculty of Medicine, Dalhousie University, 1459 Oxford Street, Halifax, NS B3H 4R2, Canada.
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Čyras K, Oliveira T, Karamlou A, Toni F. Assumption-based argumentation with preferences and goals for patient-centric reasoning with interacting clinical guidelines. ARGUMENT & COMPUTATION 2021. [DOI: 10.3233/aac-200523] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
A paramount, yet unresolved issue in personalised medicine is that of automated reasoning with clinical guidelines in multimorbidity settings. This entails enabling machines to use computerised generic clinical guideline recommendations and patient-specific information to yield patient-tailored recommendations where interactions arising due to multimorbidities are resolved. This problem is further complicated by patient management desiderata, in particular the need to account for patient-centric goals as well as preferences of various parties involved. We propose to solve this problem of automated reasoning with interacting guideline recommendations in the context of a given patient by means of computational argumentation. In particular, we advance a structured argumentation formalism ABA+G (short for Assumption-Based Argumentation with Preferences (ABA+) and Goals) for integrating and reasoning with information about recommendations, interactions, patient’s state, preferences and prioritised goals. ABA+G combines assumption-based reasoning with preferences and goal-driven selection among reasoning outcomes. Specifically, we assume defeasible applicability of guideline recommendations with the general goal of patient well-being, resolve interactions (conflicts and otherwise undesirable situations) among recommendations based on the state and preferences of the patient, and employ patient-centered goals to suggest interaction-resolving, goal-importance maximising and preference-adhering recommendations. We use a well-established Transition-based Medical Recommendation model for representing guideline recommendations and identifying interactions thereof, and map the components in question, together with the given patient’s state, prioritised goals, and preferences over actions, to ABA+G for automated reasoning. In this, we follow principles of patient management and establish corresponding theoretical properties as well as illustrate our approach in realistic personalised clinical reasoning scenaria.
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Affiliation(s)
- Kristijonas Čyras
- Department of Computing, Imperial College London, United Kingdom. E-mail:
| | | | - Amin Karamlou
- Department of Computing, Imperial College London, United Kingdom. E-mails: ,
| | - Francesca Toni
- Department of Computing, Imperial College London, United Kingdom. E-mails: ,
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Towards a goal-oriented methodology for clinical-guideline-based management recommendations for patients with multimorbidity: GoCom and its preliminary evaluation. J Biomed Inform 2020; 112:103587. [PMID: 33035704 DOI: 10.1016/j.jbi.2020.103587] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 09/03/2020] [Accepted: 09/06/2020] [Indexed: 11/23/2022]
Abstract
Patients with chronic multimorbidity are becoming more common as life expectancy increases, making it necessary for physicians to develop complex management plans. We are looking at the patient management process as a goal-attainment problem. Hence, our aim is to develop a goal-oriented methodology for providing decision support for managing patients with multimorbidity continuously, as the patient's health state is progressing and new goals arise (e.g., treat ulcer, prevent osteoporosis). Our methodology allows us to detect and mitigate inconsistencies among guideline recommendations stemming from multiple clinical guidelines, while consulting medical ontologies and terminologies and relying on patient information standards. This methodology and its implementation as a decision-support system, called GoCom, starts with computer-interpretable clinical guidelines (CIGs) for single problems that are formalized using the PROforma CIG language. We previously published the architecture of the system as well as a CIG elicitation guide for enriching PROforma tasks with properties referring to vocabulary codes of goals and physiological effects of management plans. In this paper, we provide a formalization of the conceptual model of GoCom that generates, for each morbidity of the patient, a patient-specific goal tree that results from the PROforma engine's enactment of the CIG with the patient's data. We also present the "Controller" algorithm that drives the GoCom system. Given a new problem that a patient develops, the Controller detects inconsistencies among goals pertaining to different comorbid problems and consults the CIGs to generate alternative non-conflicted and goal-oriented management plans that address the multiple goals simultaneously. In this stage of our research, the inconsistencies that can be detected are of two types - starting vs. stopping medications that belong to the same medication class hierarchy, and detecting opposing physiological effect goals that are specified in concurrent CIGs (e.g., decreased blood pressure vs. increased blood pressure). However, the design of GoCom is modular and generic and allows the future introduction of additional interaction detection and mitigation strategies. Moreover, GoCom generates explanations of the alternative non-conflicted management plans, based on recommendations stemming from the clinical guidelines and reasoning patterns. GoCom's functionality was evaluated using three cases of multimorbidity interactions that were checked by our three clinicians. Usefulness was evaluated with two studies. The first evaluation was a pilot study with ten 6th year medical students and the second evaluation was done with 27 6th medical students and interns. The participants solved complex realistic cases of multimorbidity patients: with and without decision-support, two cases in the first evaluation and 6 cases in the second evaluation. Use of GoCom increased completeness of the patient management plans produced by the medical students from 0.44 to 0.71 (P-value of 0.0005) in the first evaluation, and from 0.31 to 0.78 (P-value < 0.0001) in the second evaluation. Correctness in the first evaluation was very high with (0.98) or without the system (0.91), with non-significant difference (P-value ≥ 0.17). In the second evaluation, use of GoCom increased correctness from 0.68 to 0.83 (P-value of 0.001). In addition, GoCom's explanation and visualization were perceived as useful by the vast majority of participants. While GoCom's detection of goal interactions is currently limited to detection of starting vs. stopping the same medication or medication subclasses and detecting conflicting physiological effects of concurrent medications, the evaluation demonstrated potential of the system for improving clinical decision-making for multimorbidity patients.
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Piovesan L, Terenziani P, Theseider Dupré D. Conformance analysis for comorbid patients in Answer Set Programming. J Biomed Inform 2020; 103:103377. [PMID: 32001389 DOI: 10.1016/j.jbi.2020.103377] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Revised: 01/07/2020] [Accepted: 01/10/2020] [Indexed: 11/16/2022]
Abstract
The treatment of comorbid patients is a hot problem in Medical Informatics, since the plain application of multiple Computer-Interpretable Guidelines (CIGs) can lead to interactions that are potentially dangerous for the patients. The specialized literature has mostly focused on the "a priori" or "execution-time" analysis of the interactions between multiple Computer-Interpretable Guidelines (CIGs), and/or CIG "merge". In this paper, we face a complementary problem, namely, the a posteriori analysis of the treatment of comorbid patients. Given the CIGs, the history of the status of the patient, and the log of the clinical actions executed on her, we try to explain the actions executed on the patient (i.e., the log) in terms of the actions recommended by the CIGs, of their potential interactions, and of the possible ways of managing each such interaction, pointing out (i) deviations from CIG recommendations not explained in terms of interaction management (if any) and (ii) unmanaged interactions (if any). Our approach is based on Answer Set Programming, and, to face realistic problems, devotes specific attention to the temporal dimension.
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Affiliation(s)
- Luca Piovesan
- DISIT, Institute of Computer Science, Università del Piemonte Orientale, Alessandria, Italy.
| | - Paolo Terenziani
- DISIT, Institute of Computer Science, Università del Piemonte Orientale, Alessandria, Italy.
| | - Daniele Theseider Dupré
- DISIT, Institute of Computer Science, Università del Piemonte Orientale, Alessandria, Italy.
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Abstract
OBJECTIVES This survey analyses the latest literature contributions to clinical decision support systems (DSSs) on a two-year period (2017-2018), focusing on the approaches that adopt Artificial Intelligence (AI) techniques in a broad sense. The goal is to analyse the distribution of data-driven AI approaches with respect to "classical" knowledge-based ones, and to consider the issues raised and their possible solutions. METHODS We included PubMed and Web of ScienceTM publications, focusing on contributions describing clinical DSSs that adopted one or more AI methodologies. RESULTS We selected 75 papers, 49 of which describe approaches in the data-driven AI area, 20 present purely knowledge-based DSSs, and 6 adopt hybrid approaches relying on both formalized knowledge and data. CONCLUSIONS Recent studies in the clinical DSS area demonstrate a prevalence of data-driven AI, which can be adopted autonomously in purely data-driven systems, or in cooperation with domain knowledge in hybrid systems. Such hybrid approaches, able to conjugate all available knowledge sources through proper knowledge integration steps, represent an interesting example of synergy between the two AI categories. This synergy can lead to the resolution of some existing issues, such as the need for transparency and explainability, nowadays recognized as central themes to be addressed by both AI and medical informatics research.
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Affiliation(s)
- Stefania Montani
- DISIT, Computer Science Institute, University of Piemonte Orientale, Alessandria, Italy
| | - Manuel Striani
- DISIT, Computer Science Institute, University of Piemonte Orientale, Alessandria, Italy
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Bowles J, Caminati M, Cha S, Mendoza J. A framework for automated conflict detection and resolution in medical guidelines. SCIENCE OF COMPUTER PROGRAMMING 2019; 182:42-63. [PMID: 32029957 PMCID: PMC6993806 DOI: 10.1016/j.scico.2019.07.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2018] [Revised: 06/09/2019] [Accepted: 07/01/2019] [Indexed: 05/30/2023]
Abstract
Common chronic conditions are routinely treated following standardised procedures known as clinical guidelines. For patients suffering from two or more chronic conditions, known as multimorbidity, several guidelines have to be applied simultaneously, which may lead to severe adverse effects when the combined recommendations and prescribed medications are inconsistent or incomplete. This paper presents an automated formal framework to detect, highlight and resolve conflicts in the treatments used for patients with multimorbidities focusing on medications. The presented extended framework has a front-end which takes guidelines captured in a standard modelling language and returns the visualisation of the detected conflicts as well as suggested alternative treatments. Internally, the guidelines are transformed into formal models capturing the possible unfoldings of the guidelines. The back-end takes the formal models associated with multiple guidelines and checks their correctness with a theorem prover, and inherent inconsistencies with a constraint solver. Key to our approach is the use of an optimising constraint solver which enables us to search for the best solution that resolves/minimises conflicts according to medication efficacy and the degree of severity in case of harmful combinations, also taking into account their temporal overlapping. The approach is illustrated throughout with a real medical example.
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Affiliation(s)
- J. Bowles
- School of Computer Science, University of St Andrews, Jack Cole Building, St Andrews KY16 9SX, United Kingdom
| | - M.B. Caminati
- School of Computer Science, University of St Andrews, Jack Cole Building, St Andrews KY16 9SX, United Kingdom
| | - S. Cha
- Automation and Information Systems, Technical University of Munich, Germany
| | - J. Mendoza
- School of Computer Science, University of St Andrews, Jack Cole Building, St Andrews KY16 9SX, United Kingdom
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Fdez-Olivares J, Onaindia E, Castillo L, Jordán J, Cózar J. Personalized conciliation of clinical guidelines for comorbid patients through multi-agent planning. Artif Intell Med 2019; 96:167-186. [DOI: 10.1016/j.artmed.2018.11.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Revised: 09/24/2018] [Accepted: 11/06/2018] [Indexed: 10/27/2022]
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Jafarpour B, Raza Abidi S, Van Woensel W, Raza Abidi SS. Execution-time integration of clinical practice guidelines to provide decision support for comorbid conditions. Artif Intell Med 2019; 94:117-137. [PMID: 30871678 DOI: 10.1016/j.artmed.2019.02.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2017] [Revised: 11/30/2018] [Accepted: 02/17/2019] [Indexed: 01/11/2023]
Abstract
Patients with multiple medical conditions (comorbidity) pose major challenges to clinical decision support systems, since the different Clinical Practice Guidelines (CPG) often involve adverse interactions, such as drug-drug or drug-disease interactions. Moreover, opportunities often exist for optimizing care and resources across multiple CPG. These challenges have been taken up in the state of the art, with many approaches focusing on the static integration of comorbid CIG. Nevertheless, we observe that many aspects often change dynamically over time, in ways that cannot be foreseen - such as delays in care tasks, resource availability, test outcomes, and acute comorbid conditions. To ensure the clinical safety and effectiveness of integrating multiple comorbid CIG, these execution-time difficulties must be considered. Further, when dealing with comorbid conditions, we remark that clinical practitioners typically consider multiple complex solutions, depending on the patient's health profile. Hence, execution-time flexibility, based on dynamic health parameters, is needed to effectively and safely cope with comorbid conditions. In this work, we introduce a flexible, knowledge-driven and execution-time approach to comorbid CIG integration, based on an OWL ontology with clearly defined integration semantics.
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Affiliation(s)
- Borna Jafarpour
- Faculty of Computer Science, Dalhousie University, 6050 University Ave, Halifax, NS, B3H 1W5, Canada.
| | - Samina Raza Abidi
- Faculty of Medicine, Dalhousie University, 1459 Oxford Street, Halifax, NS B3H 4R2, Canada.
| | - William Van Woensel
- Faculty of Computer Science, Dalhousie University, 6050 University Ave, Halifax, NS, B3H 1W5, Canada.
| | - Syed Sibte Raza Abidi
- Faculty of Computer Science, Dalhousie University, 6050 University Ave, Halifax, NS, B3H 1W5, Canada.
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Bilici E, Despotou G, Arvanitis TN. The use of computer-interpretable clinical guidelines to manage care complexities of patients with multimorbid conditions: A review. Digit Health 2018; 4:2055207618804927. [PMID: 30302270 PMCID: PMC6172935 DOI: 10.1177/2055207618804927] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Accepted: 09/05/2018] [Indexed: 01/25/2023] Open
Abstract
Clinical practice guidelines (CPGs) document evidence-based information and recommendations on treatment and management of conditions. CPGs usually focus on management of a single condition; however, in many cases a patient will be at the centre of multiple health conditions (multimorbidity). Multiple CPGs need to be followed in parallel, each managing a separate condition, which often results in instructions that may interact with each other, such as conflicts in medication. Furthermore, the impetus to deliver customised care based on patient-specific information, results in the need to be able to offer guidelines in an integrated manner, identifying and managing their interactions. In recent years, CPGs have been formatted as computer-interpretable guidelines (CIGs). This enables developing CIG-driven clinical decision support systems (CDSSs), which allow the development of IT applications that contribute to the systematic and reliable management of multiple guidelines. This study focuses on understanding the use of CIG-based CDSSs, in order to manage care complexities of patients with multimorbidity. The literature between 2011 and 2017 is reviewed, which covers: (a) the challenges and barriers in the care of multimorbid patients, (b) the role of CIGs in CDSS augmented delivery of care, and (c) the approaches to alleviating care complexities of multimorbid patients. Generating integrated care plans, detecting and resolving adverse interactions between treatments and medications, dealing with temporal constraints in care steps, supporting patient-caregiver shared decision making and maintaining the continuity of care are some of the approaches that are enabled using a CIG-based CDSS.
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Affiliation(s)
- Eda Bilici
- Institute of Digital Healthcare, WMG, University of Warwick, UK
| | - George Despotou
- Institute of Digital Healthcare, WMG, University of Warwick, UK
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Artificial Intelligence in Medicine AIME 2015. Artif Intell Med 2017; 81:1-2. [PMID: 28733119 DOI: 10.1016/j.artmed.2017.06.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2017] [Accepted: 06/29/2017] [Indexed: 11/23/2022]
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