Preclude2 : Personalized conflict detection in heterogeneous health applications

Abstract Conflicting health information is a primary barrier of self-management of chronic diseases. Increasing number of people now rely on mobile health apps and online health websites to meet their information needs and often receive conflicting health advice from these sources. This problem is more prevalent and severe in the setting of multi-morbidities. In addition, often medical information can be conflicting with regular activity patterns of an individual. In this work, we formulate the problem of finding conflicts in heterogeneous health applications including health websites, health apps, online drug usage guidelines, and daily activity logging applications. We develop a comprehensive taxonomy of conflicts based on the semantics of textual health advice and activities of daily living. Finding conflicts in health applications poses its own unique lexical and semantic challenges. These include large structural variation between pairs of textual advice, finding conceptual overlap between pairs of advice, inferring the semantics of an advice (i.e., what to do, why and how) and activities, and aligning activities suggested in advice with the activities of daily living based on their underlying dependencies and polarity. Hence, we develop Preclude2, a novel semantic rule-based solution to detect conflicts in activities and health advice derived from heterogeneous sources. Preclude2 utilizes linguistic rules and external knowledge bases to infer advice. In addition, Preclude2 considers personalization and context-awareness while detecting conflicts. We evaluate Preclude2 using 1156 real advice statements covering 8 important health topics, 90 online drug usage guidelines, 1124 online disease specific health advice covering 34 chronic diseases, and 2 activity datasets. The evaluation is personalized based on 34 real prescriptions. Preclude2 detects direct, conditional, sub-typical, quantitative, and temporal conflicts from 2129 advice statements with 0.91, 0.83, 0.98, 0.85 and 0.98 recall, respectively. Overall, it results in 0.88 recall for detecting inter advice conflicts and 0.89 recall for detecting activity–advice conflicts. We also demonstrate the effects of personalization and context-awareness in conflict detection from heterogeneous health applications.