Leveraging Large Language Models for Predicting Postoperative Acute Kidney Injury in Elderly Patients.

ABSTRACT: Objective: The objective of this work is to develop a framework based on large language models (LLMs) to predict postoperative acute kidney injury (AKI) outcomes in elderly patients. Impact Statement: Our study demonstrates that LLMs have the potential to address the issues of poor generalization and weak interpretability commonly encountered in disease prediction using traditional machine learning (ML) models. Introduction: AKI is a severe postoperative complication, especially in elderly patients with declining renal function. Current AKI prediction models rely on ML, but their lack of interpretability and generalizability limits clinical use. LLMs, with extensive pretraining and text generation capabilities, offer a new solution. Methods: We applied prompt en

SUBMITTER: Zhu H 

PROVIDER: S-EPMC11896637 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature