Clinical Use of Nomogram Based on Machine Learning for Diagnosis Prediction of Acute Respiratory Distress Syndrome in Patients With Acute Pancreatitis.

ABSTRACT:

Background

This study focused on utilizing machine learning techniques to construct a predictive nomograph for early identification of acute pancreatitis (AP) patients at risk of developing acute respiratory distress syndrome (ARDS).

Methods

We retrospectively analyzed 427 AP patients from Tongji Hospital (2010-2021) and externally validated the model using the MIMIC-IV database. Six machine learning algorithms were compared, with the support vector machine (SVM) selected for nomogram construction. Key predictors included age, sex, SOFA score, C-reactive protein (CRP), platelet count (PLT), total bilirubin (TBIL), and direct bilirubin (DBIL). Model performance was assessed via area under the curve (AUC), calibration curves, and decision curve analysis (DCA).

Results

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SUBMITTER: Hu H 

PROVIDER: S-EPMC12643695 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature