Large Language Models: A Generalizable and Interpretable Approach for Postoperative Risk Prediction in Elderly Surgical Patients (Motivated by the AKI Prediction Framework Study Using LLMs by Zhu et al.)

Abstract

This report explores the application of large language models as a generalizable and interpretable solution for predicting postoperative kidney injury in elderly surgical patients, motivated by the recent study by Zhu et al. Traditional machine learning models, while widely used in clinical prediction, often suffer from poor interpretability and reduced generalizability when deployed across heterogeneous clinical settings. Large language models, in contrast, offer a unique advantage through their ability to generate human-readable rationales alongside accurate predictions, improving clinical trust and transparency. In the featured study, a structured framework was developed to translate tabular clinical data into narrative inputs processed by commercial and open-source language models. Prompt engineering and fine-tuning enabled these models to outperform conventional algorithms, particularly on external datasets, while also offering clear justifications for their outputs. This report highlights key components of the framework, including dataset requirements, performance bench-marking, and illustrative case outputs. Through comparative tables and plots, we demonstrate how language models deliver both accuracy and adaptability in high-risk surgical populations. Strengths, limitations, and future research directions are also discussed, emphasizing the role of language models in supporting scalable, interpret-able, and clinically meaningful prediction tools in real-world healthcare environments.

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