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| Hauptverfasser: | , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2601.02106 |
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| _version_ | 1866911355152367616 |
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| author | Rana, Ashish Shaker, Ammar Saralajew, Sascha Suzuki, Takashi Yasuda, Kosuke Kato, Shintaro Wada, Toshikazu Fujikawa, Toshiyuki Kikutsuji, Toru |
| author_facet | Rana, Ashish Shaker, Ammar Saralajew, Sascha Suzuki, Takashi Yasuda, Kosuke Kato, Shintaro Wada, Toshikazu Fujikawa, Toshiyuki Kikutsuji, Toru |
| contents | Despite recent advances in machine learning and explainable AI, a gap remains in personalized preventive healthcare: predictions, interventions, and recommendations should be both understandable and verifiable for all stakeholders in the healthcare sector. We present a demonstration of how prototype-based learning can address these needs. Our proposed framework, ProtoPal, features both front- and back-end modes; it achieves superior quantitative performance while also providing an intuitive presentation of interventions and their simulated outcomes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_02106 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Prototype-Based Learning for Healthcare: A Demonstration of Interpretable AI Rana, Ashish Shaker, Ammar Saralajew, Sascha Suzuki, Takashi Yasuda, Kosuke Kato, Shintaro Wada, Toshikazu Fujikawa, Toshiyuki Kikutsuji, Toru Machine Learning Despite recent advances in machine learning and explainable AI, a gap remains in personalized preventive healthcare: predictions, interventions, and recommendations should be both understandable and verifiable for all stakeholders in the healthcare sector. We present a demonstration of how prototype-based learning can address these needs. Our proposed framework, ProtoPal, features both front- and back-end modes; it achieves superior quantitative performance while also providing an intuitive presentation of interventions and their simulated outcomes. |
| title | Prototype-Based Learning for Healthcare: A Demonstration of Interpretable AI |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2601.02106 |