We demonstrate an end-to-end argumentation-assisted decision support system (DSS) to provide patient-centric, non-conflicting clinical guideline recommendations in multimorbidity settings. Specifically, our system integrates an electronic health record (EHR) component to identify patient-tailored guideline recommendations represented in the computer-interpretable Transition-based Medical Recommendations (TMR) model, and an argumentation methodology (from Artificial Intelligence) to reason with conflicting patient-specific recommendations and various preferences. The system transparently yields individual clinical recommendations together with the underlying arguments considered, thus providing reasons for and against the suggested decisions. Following the Learning Health Systems (LHS) paradigm and aiming for our argumentation-assisted DSS to learn and adapt from the existing evidence, we intend for a design that allows our system to take into account the ‘expected’ outcomes of health policies and their changes.
Learning Objective: After participating in this session, the learner should be better able to:
1. Appreciate the challenges in automatically tailoring to patients and combining multiple clinical guideline recommendations to support decision making in multimorbidity settings.
2. Learn about the advantages offered by argumentation techniques from Artificial Intelligence in reasoning with, and explaining decisions based on, clinical guidelines and patient-specific information.
3. Understand the need for creating and populating repositories with computer-interpretable guidelines, as well as on capturing and storing new types of information on or alongside electronic health records.
Kristijonas Cyras, Imperial College London
Jesus Dominguez (Presenter)
King's College London
Amin Karamlou, Imperial College London
Denys Prociuk, Imperial College London
Vasa Curcin, King's College London
Brendan Delaney, Imperial College London
Francesca Toni, Imperial College London
Kalipso Chalkidou, Imperial College London
Ara Darzi, Imperial College London