Interpretable machine learning enables early and accurate detection of drug-induced liver injury: A multicenter study with real-world clinical translation

Abstract

Drug-induced liver injury (DILI) remains a major clinical challenge due to the absence of specific biomarkers and dependence on subjective diagnostic criteria. This study presents an interpretable gradient-boosting decision tree model (XGB-D) that uses routine laboratory data to enable early and accurate DILI detection. Developed through a multicenter cohort of 36,199 patients, XGB-D shows superior diagnostic performance (area under the curve [AUC] = 0.971) compared with conventional methods and demonstrates robust generalizability across three independent validation cohorts (AUC = 0.881–0.935). SHAP (SHapley Additive exPlanations) analysis identifies alanine aminotransferase and C-reactive protein as key contributors, revealing mechanistic links between hepatocellular damage and inflammatory responses. In prospective real-world monitoring, XGB-D detected DILI signals 2–4 weeks earlier than expert assessment in 29.4% of cases, supporting timely clinical intervention. By integrating interpretable machine learning with clinical hepatology, this work establishes a scalable and transparent framework for precision toxicology with significant implications for drug safety evaluation and personalized medicine.

Keywords

artificial intelligence / clinical laboratory indicators / diagnosis / drug-induced liver injury

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2026 The Author(s). Interdisciplinary Medicine published by Wiley-VCH GmbH on behalf of Nanfang Hospital, Southern Medical University.