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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2601.19824 |
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| _version_ | 1866912853541257216 |
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| author | de Lima, Andre Paulino Castro, Paula de Andrade, Suzana Carvalho Vaz Marcucci, Rosa Maria de Melo, Ruth Caldeira Manzato, Marcelo Garcia |
| author_facet | de Lima, Andre Paulino Castro, Paula de Andrade, Suzana Carvalho Vaz Marcucci, Rosa Maria de Melo, Ruth Caldeira Manzato, Marcelo Garcia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_19824 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | An Interpretable Recommendation Model for Psychometric Data, With an Application to Gerontological Primary Care de Lima, Andre Paulino Castro, Paula de Andrade, Suzana Carvalho Vaz Marcucci, Rosa Maria de Melo, Ruth Caldeira Manzato, Marcelo Garcia Artificial Intelligence Human-Computer Interaction Information Retrieval Social and Information Networks H.3.3; J.3 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. |
| title | An Interpretable Recommendation Model for Psychometric Data, With an Application to Gerontological Primary Care |
| topic | Artificial Intelligence Human-Computer Interaction Information Retrieval Social and Information Networks H.3.3; J.3 |
| url | https://arxiv.org/abs/2601.19824 |