为老年全科医疗设计可解释推荐模型，助力个性化护理计划制定
An Interpretable Recommendation Model for Psychometric Data, With an Application to Gerontological Primary Care
- 利用心理测量数据结构生成可视化解释，提升医生理解度
- 在巴西合作机构数据集上表现优于基线模型，用户研究验证解释有效性
- 适合老年医疗、临床决策支持等需要可解释AI的场景
将推荐系统应用于医疗环境面临诸多挑战：临床数据难获取、推荐原因难以理解、执行风险高且效果不确定。本文针对老年全科医疗这一细分领域，提出一种基于心理测量数据结构的可解释推荐模型，通过可视化解释帮助医护人员理解推荐依据，辅助制定个性化护理方案。在巴西研究伙伴提供的医疗数据集上进行了离线性能对比评估，并开展用户研究验证模型生成解释的可读性。结果表明，该模型能有效推动推荐系统在该医疗领域的应用，而随着人口老龄化加剧，该领域对信息化与个性化服务的需求将持续增长。
原文摘要 · Abstract (English)
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.
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