Clinical notes recorded during a patient's perioperative journey holds immense informational value. Advances in large language models (LLMs) offer opportunities for bridging this gap. Using 84,875 pre-operative notes and its associated surgical cases from 2018 to 2021, we examine the performance of LLMs in predicting six postoperative risks using various fine-tuning strategies. Pretrained LLMs outperformed traditional word embeddings by an absolute AUROC of 38.3% and AUPRC of 33.2%. Self-supervised fine-tuning further improved performance by 3.2% and 1.5%. Incorporating labels into training further increased AUROC by 1.8% and AUPRC by 2%. The highest performance was achieved with a unified foundation model, with improvements of 3.6% for AUROC and 2.6% for AUPRC compared to self-supervision, highlighting the foundational capabilities of LLMs in predicting postoperative risks, which could be potentially beneficial when deployed for perioperative care
@article{arxiv.2402.17493,
title = {The Foundational Capabilities of Large Language Models in Predicting Postoperative Risks Using Clinical Notes},
author = {Charles Alba and Bing Xue and Joanna Abraham and Thomas Kannampallil and Chenyang Lu},
journal= {arXiv preprint arXiv:2402.17493},
year = {2025}
}
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Codes are publicly available at: https://github.com/cja5553/LLMs_in_perioperative_care