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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.13225 |
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| _version_ | 1866910337710686208 |
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| author | Jin, Qiao Wang, Zhizheng Yang, Yifan Zhu, Qingqing Wright, Donald Huang, Thomas Wilbur, W John He, Zhe Taylor, Andrew Chen, Qingyu Lu, Zhiyong |
| author_facet | Jin, Qiao Wang, Zhizheng Yang, Yifan Zhu, Qingqing Wright, Donald Huang, Thomas Wilbur, W John He, Zhe Taylor, Andrew Chen, Qingyu Lu, Zhiyong |
| contents | Clinical calculators play a vital role in healthcare by offering accurate evidence-based predictions for various purposes such as prognosis. Nevertheless, their widespread utilization is frequently hindered by usability challenges, poor dissemination, and restricted functionality. Augmenting large language models with extensive collections of clinical calculators presents an opportunity to overcome these obstacles and improve workflow efficiency, but the scalability of the manual curation process poses a significant challenge. In response, we introduce AgentMD, a novel language agent capable of curating and applying clinical calculators across various clinical contexts. Using the published literature, AgentMD has automatically curated a collection of 2,164 diverse clinical calculators with executable functions and structured documentation, collectively named RiskCalcs. Manual evaluations show that RiskCalcs tools achieve an accuracy of over 80% on three quality metrics. At inference time, AgentMD can automatically select and apply the relevant RiskCalcs tools given any patient description. On the newly established RiskQA benchmark, AgentMD significantly outperforms chain-of-thought prompting with GPT-4 (87.7% vs. 40.9% in accuracy). Additionally, we also applied AgentMD to real-world clinical notes for analyzing both population-level and risk-level patient characteristics. In summary, our study illustrates the utility of language agents augmented with clinical calculators for healthcare analytics and patient care. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_13225 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AgentMD: Empowering Language Agents for Risk Prediction with Large-Scale Clinical Tool Learning Jin, Qiao Wang, Zhizheng Yang, Yifan Zhu, Qingqing Wright, Donald Huang, Thomas Wilbur, W John He, Zhe Taylor, Andrew Chen, Qingyu Lu, Zhiyong Computation and Language Artificial Intelligence Clinical calculators play a vital role in healthcare by offering accurate evidence-based predictions for various purposes such as prognosis. Nevertheless, their widespread utilization is frequently hindered by usability challenges, poor dissemination, and restricted functionality. Augmenting large language models with extensive collections of clinical calculators presents an opportunity to overcome these obstacles and improve workflow efficiency, but the scalability of the manual curation process poses a significant challenge. In response, we introduce AgentMD, a novel language agent capable of curating and applying clinical calculators across various clinical contexts. Using the published literature, AgentMD has automatically curated a collection of 2,164 diverse clinical calculators with executable functions and structured documentation, collectively named RiskCalcs. Manual evaluations show that RiskCalcs tools achieve an accuracy of over 80% on three quality metrics. At inference time, AgentMD can automatically select and apply the relevant RiskCalcs tools given any patient description. On the newly established RiskQA benchmark, AgentMD significantly outperforms chain-of-thought prompting with GPT-4 (87.7% vs. 40.9% in accuracy). Additionally, we also applied AgentMD to real-world clinical notes for analyzing both population-level and risk-level patient characteristics. In summary, our study illustrates the utility of language agents augmented with clinical calculators for healthcare analytics and patient care. |
| title | AgentMD: Empowering Language Agents for Risk Prediction with Large-Scale Clinical Tool Learning |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2402.13225 |