An Interpretable Recommendation Model for Psychometric Data, With an Application to Gerontological Primary Care

TL;DR

This paper introduces an interpretable recommendation model tailored for psychometric data in gerontological primary care, providing visual explanations to assist healthcare professionals in creating personalized care plans, supported by offline performance and user studies.

Contribution

The work presents a novel recommendation model that leverages psychometric data structure to generate faithful, interpretable visual explanations specifically for gerontological primary care.

Findings

The model performs well on healthcare datasets from Brazil.

User study confirms the interpretability of visual explanations.

The approach supports personalized care planning in gerontology.

Abstract

There are challenges that must be overcome to make recommender systems useful in healthcare settings. The reasons are varied: the lack of publicly available clinical data, the difficulty that users may have in understanding the reasons why a recommendation was made, the risks that may be involved in following that recommendation, and the uncertainty about its effectiveness. In this work, we address these challenges with a recommendation model that leverages the structure of psychometric data to provide visual explanations that are faithful to the model and interpretable by care professionals. We focus on a narrow healthcare niche, gerontological primary care, to show that the proposed recommendation model can assist the attending professional in the creation of personalised care plans. We report results of a comparative offline performance evaluation of the proposed model on healthcare datasets that were collected by research partners in Brazil, as well as the results of a user study that evaluates the interpretability of the visual explanations the model generates. The results suggest that the proposed model can advance the application of recommender systems in this healthcare niche, which is expected to grow in demand , opportunities, and information technology needs as demographic changes become more pronounced.