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A Data- and Expert-driven Decision Support Framework for Helping Patients Adhere to Therapy: Psychobehavioral Targets and Associated Interventions

Wilk, Syzmon, O'Sullivan, Dympna, Michalowski, Martin, Bonaccio, Silvia, Michalowski, Wojtek, Peleg, Mor and Carrier, Marc (2017) A Data- and Expert-driven Decision Support Framework for Helping Patients Adhere to Therapy: Psychobehavioral Targets and Associated Interventions. In: Proceedings of the International Joint Workshop on Knowledge Representation for Health Care, Process-Oriented Information Systems in Health Care, Extraction and Processing of Rich Semantics from Medical Texts. MET Project, pp. 53-66.

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Abstract

Patient adherence to therapy is one of the key determinants of treatment success, while low levels of adherence potentially lead to the worsening of health outcomes and to increased health care costs. The automatic identification of psychobehavioral targets, i.e., patterns in patients’ psychological characteristics and behaviors that positively or negatively affect their adherence level, should help physicians develop therapies that influence adherence and improve health outcomes. These targets can also be used to develop psychobehavioral interventions, i.e., plans of actions to modify patients’ behavior and psychological stance, that maintain or improve adherence level by motivating patients to avoid negative psychobehavioral targets or to achieve positive ones. In this work, we propose a theoretical decision support framework that helps patients better adhere to therapy by automatically identifying psychobehavioral targets and selecting corresponding psychobehavioral interventions. We use datadriven techniques to discover these targets from patient data. Specifically, we apply dominance-based rough set theory to induce decision rules from data and then automatically extract targets from these rules. Once these targets are identified, we apply expert knowledge to select the most appropriate generic psychobehavioral interventions and customize them to patient characteristics. We illustrate the proposed framework using a case study of patients with atrial fibrillation who follow oral anticoagulation therapy. We describe the psychobehavioral targets identified from data and present associated psychobehavioral interventions.

Item Type: Book Section
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

R Medicine > Healthcare Industry
Divisions: School of Computing > Staff Research and Publications
Depositing User: CAOIMHE NI MHAICIN
Date Deposited: 20 Sep 2018 19:01
Last Modified: 21 Sep 2018 11:28
URI: http://trap.ncirl.ie/id/eprint/3154

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