Large Language Model Capabilities in Perioperative Risk Prediction and Prognostication
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911746754609152 |
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| author | Chung, Philip Fong, Christine T Walters, Andrew M Aghaeepour, Nima Yetisgen, Meliha O'Reilly-Shah, Vikas N |
| author_facet | Chung, Philip Fong, Christine T Walters, Andrew M Aghaeepour, Nima Yetisgen, Meliha O'Reilly-Shah, Vikas N |
| contents | We investigate whether general-domain large language models such as GPT-4 Turbo can perform risk stratification and predict post-operative outcome measures using a description of the procedure and a patient's clinical notes derived from the electronic health record. We examine predictive performance on 8 different tasks: prediction of ASA Physical Status Classification, hospital admission, ICU admission, unplanned admission, hospital mortality, PACU Phase 1 duration, hospital duration, and ICU duration. Few-shot and chain-of-thought prompting improves predictive performance for several of the tasks. We achieve F1 scores of 0.50 for ASA Physical Status Classification, 0.81 for ICU admission, and 0.86 for hospital mortality. Performance on duration prediction tasks were universally poor across all prompt strategies. Current generation large language models can assist clinicians in perioperative risk stratification on classification tasks and produce high-quality natural language summaries and explanations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_01620 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large Language Model Capabilities in Perioperative Risk Prediction and Prognostication Chung, Philip Fong, Christine T Walters, Andrew M Aghaeepour, Nima Yetisgen, Meliha O'Reilly-Shah, Vikas N Artificial Intelligence Computation and Language We investigate whether general-domain large language models such as GPT-4 Turbo can perform risk stratification and predict post-operative outcome measures using a description of the procedure and a patient's clinical notes derived from the electronic health record. We examine predictive performance on 8 different tasks: prediction of ASA Physical Status Classification, hospital admission, ICU admission, unplanned admission, hospital mortality, PACU Phase 1 duration, hospital duration, and ICU duration. Few-shot and chain-of-thought prompting improves predictive performance for several of the tasks. We achieve F1 scores of 0.50 for ASA Physical Status Classification, 0.81 for ICU admission, and 0.86 for hospital mortality. Performance on duration prediction tasks were universally poor across all prompt strategies. Current generation large language models can assist clinicians in perioperative risk stratification on classification tasks and produce high-quality natural language summaries and explanations. |
| title | Large Language Model Capabilities in Perioperative Risk Prediction and Prognostication |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2401.01620 |