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.

翻译：在医疗健康领域应用推荐系统面临多重挑战：公开临床数据匮乏、用户难以理解推荐依据、遵循推荐可能存在的风险以及推荐效果的不确定性。本研究提出一种推荐模型应对上述挑战，该模型利用心理测量数据的结构特征，生成忠实于模型且能被护理专业人员直观解读的可视化解释。我们聚焦老年初级护理这一细分医疗场景，证明所提推荐模型能辅助执业专业人员制定个性化护理方案。基于巴西研究合作伙伴收集的医疗数据集，我们报告了所提模型的离线性能对比评估结果，以及针对模型生成可视化解释可理解性的用户调研结果。结果表明，随着人口结构变化趋势日益显著，该医疗细分领域在需求、机遇和信息技术需求方面将持续增长，而所提模型能有效推动推荐系统在该领域的应用发展。