Artificial intelligence models for predicting acute kidney injury in the intensive care unit: a systematic review of modeling methods, data utilization, and clinical applicability.

Journal: JAMIA open
Published Date: Jul 3, 2025

Abstract

OBJECTIVES: Acute kidney injury (AKI) is common in intensive care unit (ICU) patients and is associated with high mortality, prolonged ICU stays, and increased costs. Early prediction is crucial for timely intervention and improved outcomes. Various prediction models, including machine learning, deep learning, and dynamic prediction frameworks, have been developed, but their modeling approaches, data utilization, and clinical applicability require further investigation. This review comprehensively assesses the modeling methods, data utilization strategies, and clinical applicability of AKI prediction models in the ICU, identifies current challenges, and proposes future research directions.

Authors

  • Tongyue Shi
    National Institute of Health Data Science, Peking University, Beijing, China.
  • Yu Lin
    Research School of Computer Science, Australian National University, Canberra, 2601, ACT, Australia.
  • Huiying Zhao
    Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 Yan Jiang West Road, Guangzhou 510120, China.
  • Guilan Kong
    National Institute of Health Data Science, Peking University, Beijing, China. guilan.kong@hsc.pku.edu.cn.

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