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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2506.06340 |
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| _version_ | 1866918048036814848 |
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| author | Ran, Wu Hao Xi, Xi Li, Furong Lu, Jingyi Jiang, Jian Huang, Hui Zhang, Yuzhuan Li, Shi |
| author_facet | Ran, Wu Hao Xi, Xi Li, Furong Lu, Jingyi Jiang, Jian Huang, Hui Zhang, Yuzhuan Li, Shi |
| contents | The advent of large language models (LLMs) has opened new avenues for analyzing complex, unstructured data, particularly within the medical domain. Electronic Health Records (EHRs) contain a wealth of information in various formats, including free text clinical notes, structured lab results, and diagnostic codes. This paper explores the application of advanced language models to leverage these diverse data sources for improved clinical decision support. We will discuss how text-based features, often overlooked in traditional high dimensional EHR analysis, can provide semantically rich representations and aid in harmonizing data across different institutions. Furthermore, we delve into the challenges and opportunities of incorporating medical codes and ensuring the generalizability and fairness of AI models in healthcare. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06340 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Structured Semantics from Unstructured Notes: Language Model Approaches to EHR-Based Decision Support Ran, Wu Hao Xi, Xi Li, Furong Lu, Jingyi Jiang, Jian Huang, Hui Zhang, Yuzhuan Li, Shi Information Retrieval Artificial Intelligence The advent of large language models (LLMs) has opened new avenues for analyzing complex, unstructured data, particularly within the medical domain. Electronic Health Records (EHRs) contain a wealth of information in various formats, including free text clinical notes, structured lab results, and diagnostic codes. This paper explores the application of advanced language models to leverage these diverse data sources for improved clinical decision support. We will discuss how text-based features, often overlooked in traditional high dimensional EHR analysis, can provide semantically rich representations and aid in harmonizing data across different institutions. Furthermore, we delve into the challenges and opportunities of incorporating medical codes and ensuring the generalizability and fairness of AI models in healthcare. |
| title | Structured Semantics from Unstructured Notes: Language Model Approaches to EHR-Based Decision Support |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2506.06340 |