# Large Language Model Capabilities in Perioperative Risk Prediction and Prognostication

arXiv: https://arxiv.org/abs/2401.01620 · HTML: https://mlsanity.com/paper/2401.01620/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 3 Jan 2024.

## Builds on (most cited first, 3 of 3)

- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization](https://mlsanity.com/paper/2309.07430/) (2023)
- [Extracting Medication Changes in Clinical Narratives using Pre-trained Language Models](https://mlsanity.com/paper/2208.08417/) (2022)

## Built on by (most cited first, 11 of 11)

- [Solving the Unsolvable: Translating Case Law in Hong Kong](https://mlsanity.com/paper/2501.09444/) (2025)
- [The Power of Combining Data and Knowledge: GPT-4o is an Effective Interpreter of Machine Learning Models in Predicting Lymph Node Metastasis of Lung Cancer](https://mlsanity.com/paper/2407.17900/) (2024)
- [CURENet: Combining Unified Representations for Efficient Chronic Disease Prediction](https://mlsanity.com/paper/2511.11423/) (2025)
- [Interpretable Probability Estimation with LLMs via Shapley Reconstruction](https://mlsanity.com/paper/2601.09151/) (2026)
- [Stochastic Parrots or ICU Experts? Large Language Models in Critical Care Medicine: A Scoping Review](https://mlsanity.com/paper/2407.19256/) (2024)
- [Decoding Rarity: Large Language Models in the Diagnosis of Rare Diseases](https://mlsanity.com/paper/2505.17065/) (2025)
- [LLMs and Agentic AI in Insurance Decision-Making: Opportunities and Challenges For Africa](https://mlsanity.com/paper/2508.15110/) (2025)
- [Mitigating Clinician Information Overload: Generative AI for Integrated EHR and RPM Data Analysis](https://mlsanity.com/paper/2509.00073/) (2025)
- [SafeMed-R1: Clinician-Audited Safety and Ethics Alignment for Medical Large Language Models](https://mlsanity.com/paper/2605.28338/) (2026)
- [Trajectory-Aware Clinical Risk Prediction via Severity-Grounded Knowledge Graphs and Retrieval-Augmented Generation](https://mlsanity.com/paper/2607.18270/) (2026)
- [The Ceiling Is in the Channel: Auditing Learner Gaps and Measurement Frontiers in Clinical Prediction](https://mlsanity.com/paper/2609.01909/) (2026)

## Abstract (arXiv)

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.