Step-by-Step Guidance to Differential Anemia Diagnosis with Real-World Data and Deep Reinforcement Learning
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866912141995409408 |
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| author | Muyama, Lillian Lu, Estelle Cheminet, Geoffrey Pouchot, Jacques Rance, Bastien Tropeano, Anne-Isabelle Neuraz, Antoine Coulet, Adrien |
| author_facet | Muyama, Lillian Lu, Estelle Cheminet, Geoffrey Pouchot, Jacques Rance, Bastien Tropeano, Anne-Isabelle Neuraz, Antoine Coulet, Adrien |
| contents | Clinical diagnostic guidelines outline the key questions to answer to reach a diagnosis. Inspired by guidelines, we aim to develop a model that learns from electronic health records to determine the optimal sequence of actions for accurate diagnosis. Focusing on anemia and its sub-types, we employ deep reinforcement learning (DRL) algorithms and evaluate their performance on both a synthetic dataset, which is based on expert-defined diagnostic pathways, and a real-world dataset. We investigate the performance of these algorithms across various scenarios. Our experimental results demonstrate that DRL algorithms perform competitively with state-of-the-art methods while offering the significant advantage of progressively generating pathways to the suggested diagnosis, providing a transparent decision-making process that can guide and explain diagnostic reasoning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_02273 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Step-by-Step Guidance to Differential Anemia Diagnosis with Real-World Data and Deep Reinforcement Learning Muyama, Lillian Lu, Estelle Cheminet, Geoffrey Pouchot, Jacques Rance, Bastien Tropeano, Anne-Isabelle Neuraz, Antoine Coulet, Adrien Machine Learning Clinical diagnostic guidelines outline the key questions to answer to reach a diagnosis. Inspired by guidelines, we aim to develop a model that learns from electronic health records to determine the optimal sequence of actions for accurate diagnosis. Focusing on anemia and its sub-types, we employ deep reinforcement learning (DRL) algorithms and evaluate their performance on both a synthetic dataset, which is based on expert-defined diagnostic pathways, and a real-world dataset. We investigate the performance of these algorithms across various scenarios. Our experimental results demonstrate that DRL algorithms perform competitively with state-of-the-art methods while offering the significant advantage of progressively generating pathways to the suggested diagnosis, providing a transparent decision-making process that can guide and explain diagnostic reasoning. |
| title | Step-by-Step Guidance to Differential Anemia Diagnosis with Real-World Data and Deep Reinforcement Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2412.02273 |