Interpretable machine learning for early prediction of acute kidney injury in critically ill patients with acute pancreatitis - Takeaways - MDSpire

Interpretable machine learning for early prediction of acute kidney injury in critically ill patients with acute pancreatitis

  • By

  • Li Zhao

  • Lei Tian

  • Shenglin Zhou

  • Tuo Zhang

  • Zeyu Yang

  • Qiuxia Liu

  • Wei Fang

  • Jicheng Zhang

  • Man Chen

  • July 1, 2026

  • 0 min

Share

  • 1

    Acute pancreatitis (AP) can lead to acute kidney injury (AKI), which is associated with increased mortality and prolonged hospital stays.

  • 2

    Current AKI diagnosis relies on serum creatinine and urine output, which may not detect early-stage renal dysfunction in critically ill patients.

  • 3

    Machine learning (ML) has shown promise in predicting AP-AKI risk, but many models lack transparency in their decision-making processes.

  • 4

    This study aimed to develop an interpretable ML model for early AKI prediction in ICU patients with AP, using SHAP analysis for transparency.

  • 5

    The study included a retrospective cohort from Shandong Provincial Hospital and an external validation cohort from the MIMIC-IV database.

Original Source(s)

Interpretable machine learning for early prediction of acute kidney injury in critically ill patients with acute pancreatitis

  • by Li Zhao, Lei Tian, Shenglin Zhou, Tuo Zhang, Zeyu Yang, Qiuxia Liu, Wei Fang, Jicheng Zhang, Man Chen

  • July 1, 2026

Related Content

Radiomics-Based AI for the Diagnosis and Prognosis of Vessels Encapsulating Tumor Clusters in Hepatocellular Carcinoma: Systematic Review and Meta-Analysis

Interplay between MASLD, obesity and type 2 diabetes: epidemiology, shared pathways and clinical implications

Double gallbladder malformation with duodenal ulcer stricture leading to common bile duct dilation: a case report