An interpretable recommendation model for gerontological care

Abstract

Recommender systems have been successfully applied to diverse areas, but their use in the healthcare domain is still rare. One challenge of applying recommender systems to this domain is related to legal concerns about the consequences of provided recommendations. In this work, we advance an expert-in-the-loop, explanation-first approach to tackle this challenge in a specific healthcare niche: gerontological care. A key aspect of the proposed approach is that both recommendations and explanations reflect the structured questionnaire employed by the practitioner to identify patient needs. Another key aspect is that a clinical dataset of patient assessments and respective assigned interventions is used to estimate effects of alternative interventions during the recommendation process. To evaluate the feasibility of this modelling approach, an explanation style was designed with help of practitioners, and a recommendation model was devised and evaluated against a clinical dataset, which was collected by a partner research group working on gerontological primary care. When compared to other traditional recommendation models, the attained precision was competitive across several evaluation conditions. The results suggest that the proposed approach is feasible and may point new ways of adapting recommender systems to play an assistive role in health care.