Large language model driven multicenter prediction and explainable risk attribution of acute kidney injury.

Authors

Lingyi Xu, Kun Yan, Zinuo Zhang, Hong Liang, Yangdong Ruan, Damin Xu, Linger Tang, Tao Zhao, Qingqing Zhou, Youlu Zhao, Yuhui Zhang, Fude Zhou, Guopeng Zhou, Zhao Yang, Xiaoli Chen, Yulan Shen, Yunlin Feng, Guisen Li, Li Wang, Jicheng Lv, Ping Wang, Xizi Zheng, Li Yang

Published in

Nature communications. Volume 17. Issue 1. Jul 27, 2026. Epub Jul 27, 2026.

Abstract

Acute kidney injury (AKI) represents a life-threatening condition among hospitalized patients, where early prediction enables prevention. Despite advances in existing models, clinical implementation remains hindered by excessive false positive rates (70%-94%) and lack of actionable clinical insights. We conduct a multi-center retrospective cohort study and develop a two-model large language model framework: AKI-PM (Prediction Model) for predicting AKI occurrence within 24 hours and AKI-RAM (Risk Attribution Model) for providing explainable risk attribution. Using a cohort of 140,637 hospital admissions across four geographically diverse Chinese hospitals, we demonstrate that AKI-PM achieves high predictive performance in internal validation (area under curve 0.95, positive predictive value 0.68) and maintains robust generalizability across external sites after few-shot (area under curve 0.92-0.96, positive predictive value 0.69-0.74). Crucially, AKI-RAM provides structured, clinically actionable risk explanations by distinguishing modifiable from non-modifiable factors and offering tailored recommendations. In a clinical evaluation of 200 cases from four independent hospitals by six nephrologists, AKI-RAM receives high scores across eight dimensions (Likert scale: 4.18-4.88) with moderate to good inter-rater reliability (intraclass correlation coefficients: 0.680-0.803). This integrated framework addresses critical limitations in AI-driven clinical prediction by combining accuracy with interpretability, offering a scalable solution for early AKI prevention in diverse healthcare settings.

PMID:
42608399
Bibliographic data and abstract were imported from PubMed on 18 Aug 2026.

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