mlsanityData to 14 Oct 2026

The Foundational Capabilities of Large Language Models in Predicting Postoperative Risks Using Clinical Notes

arXiv 2402.17493 ↗First posted 27 Feb 2024cs.CLcited by 6

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Abstract (arXiv)

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

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 15 of its 72 references that are in the map. First posted on arXiv 27 Feb 2024.

Builds on

15 of 72 references.

References · 15

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3 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 2.

6 papers.

  1. 2024HoneyBee: A Scalable Modular Framework for Creating Multimodal Oncology Datasets with Foundational Embedding Modelscited by 6
  2. 2025Prior-informed optimization of treatment recommendation via bandit algorithms trained on large language model-processed historical recordscited by 1
  3. 2025Generative Foundation Model for Structured and Unstructured Electronic Health Recordscited by 0
  4. 2025Accelerate Scaling of LLM Finetuning via Quantifying the Coverage and Depth of Instruction Setcited by 0
  5. 2026Resource-Conscious Modeling for Next- Day Discharge Prediction Using Clinical Notescited by 0
  6. 2026Clinically Grounded Privacy Evaluation of Medical LMscited by 0