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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/2505.23055 |
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| _version_ | 1866914172133965824 |
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| author | Xiang, Zhen Hsu, Aliyah R. Zane, Austin V. Kornblith, Aaron E. Lin-Martore, Margaret J. Kaur, Jasmanpreet C. Dokiparthi, Vasuda M. Li, Bo Yu, Bin |
| author_facet | Xiang, Zhen Hsu, Aliyah R. Zane, Austin V. Kornblith, Aaron E. Lin-Martore, Margaret J. Kaur, Jasmanpreet C. Dokiparthi, Vasuda M. Li, Bo Yu, Bin |
| contents | Clinical decision-making is inherently complex and fast-paced, particularly in emergency departments (EDs) where critical, rapid and high-stakes decisions are made. Clinical Decision Rules (CDRs) are standardized evidence-based tools that combine signs, symptoms, and clinical variables into decision trees to make consistent and accurate diagnoses. CDR usage is often hindered by the clinician's cognitive load, limiting their ability to quickly recall and apply the appropriate rules. We introduce CDR-Agent, a novel LLM-based system designed to enhance ED decision-making by autonomously identifying and applying the most appropriate CDRs based on unstructured clinical notes. To validate CDR-Agent, we curated two novel ED datasets: synthetic and CDR-Bench, although CDR-Agent is applicable to non ED clinics. CDR-Agent achieves a 56.3\% (synthetic) and 8.7\% (CDR-Bench) accuracy gain relative to the standalone LLM baseline in CDR selection. Moreover, CDR-Agent significantly reduces computational overhead. Using these datasets, we demonstrated that CDR-Agent not only selects relevant CDRs efficiently, but makes cautious yet effective imaging decisions by minimizing unnecessary interventions while successfully identifying most positively diagnosed cases, outperforming traditional LLM prompting approaches. Code for our work can be found at: https://github.com/zhenxianglance/medagent-cdr-agent |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_23055 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CDR-Agent: Intelligent Selection and Execution of Clinical Decision Rules Using Large Language Model Agents Xiang, Zhen Hsu, Aliyah R. Zane, Austin V. Kornblith, Aaron E. Lin-Martore, Margaret J. Kaur, Jasmanpreet C. Dokiparthi, Vasuda M. Li, Bo Yu, Bin Machine Learning Clinical decision-making is inherently complex and fast-paced, particularly in emergency departments (EDs) where critical, rapid and high-stakes decisions are made. Clinical Decision Rules (CDRs) are standardized evidence-based tools that combine signs, symptoms, and clinical variables into decision trees to make consistent and accurate diagnoses. CDR usage is often hindered by the clinician's cognitive load, limiting their ability to quickly recall and apply the appropriate rules. We introduce CDR-Agent, a novel LLM-based system designed to enhance ED decision-making by autonomously identifying and applying the most appropriate CDRs based on unstructured clinical notes. To validate CDR-Agent, we curated two novel ED datasets: synthetic and CDR-Bench, although CDR-Agent is applicable to non ED clinics. CDR-Agent achieves a 56.3\% (synthetic) and 8.7\% (CDR-Bench) accuracy gain relative to the standalone LLM baseline in CDR selection. Moreover, CDR-Agent significantly reduces computational overhead. Using these datasets, we demonstrated that CDR-Agent not only selects relevant CDRs efficiently, but makes cautious yet effective imaging decisions by minimizing unnecessary interventions while successfully identifying most positively diagnosed cases, outperforming traditional LLM prompting approaches. Code for our work can be found at: https://github.com/zhenxianglance/medagent-cdr-agent |
| title | CDR-Agent: Intelligent Selection and Execution of Clinical Decision Rules Using Large Language Model Agents |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.23055 |