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

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. This multicenter study introduces a large language model framework for real-time prediction and explainable risk attribution of acute kidney injury. It has the potential to support early clinical detection and prevention.