MedDec: A Dataset for Extracting Medical Decisions from Discharge Summaries
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909294117519360 |
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| author | Elgaar, Mohamed Cheng, Jiali Vakil, Nidhi Amiri, Hadi Celi, Leo Anthony |
| author_facet | Elgaar, Mohamed Cheng, Jiali Vakil, Nidhi Amiri, Hadi Celi, Leo Anthony |
| contents | Medical decisions directly impact individuals' health and well-being. Extracting decision spans from clinical notes plays a crucial role in understanding medical decision-making processes. In this paper, we develop a new dataset called "MedDec", which contains clinical notes of eleven different phenotypes (diseases) annotated by ten types of medical decisions. We introduce the task of medical decision extraction, aiming to jointly extract and classify different types of medical decisions within clinical notes. We provide a comprehensive analysis of the dataset, develop a span detection model as a baseline for this task, evaluate recent span detection approaches, and employ a few metrics to measure the complexity of data samples. Our findings shed light on the complexities inherent in clinical decision extraction and enable future work in this area of research. The dataset and code are available through https://github.com/CLU-UML/MedDec. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_12980 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MedDec: A Dataset for Extracting Medical Decisions from Discharge Summaries Elgaar, Mohamed Cheng, Jiali Vakil, Nidhi Amiri, Hadi Celi, Leo Anthony Computation and Language Machine Learning Medical decisions directly impact individuals' health and well-being. Extracting decision spans from clinical notes plays a crucial role in understanding medical decision-making processes. In this paper, we develop a new dataset called "MedDec", which contains clinical notes of eleven different phenotypes (diseases) annotated by ten types of medical decisions. We introduce the task of medical decision extraction, aiming to jointly extract and classify different types of medical decisions within clinical notes. We provide a comprehensive analysis of the dataset, develop a span detection model as a baseline for this task, evaluate recent span detection approaches, and employ a few metrics to measure the complexity of data samples. Our findings shed light on the complexities inherent in clinical decision extraction and enable future work in this area of research. The dataset and code are available through https://github.com/CLU-UML/MedDec. |
| title | MedDec: A Dataset for Extracting Medical Decisions from Discharge Summaries |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2408.12980 |