Developing a predictive model for septic shock risk in acute pancreatitis patients using interpretable machine learning algorithms

ML approaches exhibited promising performance in predicting septic shock in patients with AP. These models may aid in guiding treatment decisions for patients with AP in the emergency department.

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Publication Details

Journal
Digital Health
Published
2025-05-01
DOI
https://doi.org/10.1177/20552076251346361
Citations
4
Primary Topic
Pancreatitis Pathology and Treatment
Type
article
Field-Weighted Citation Impact
8.03
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article

Developing a predictive model for septic shock risk in acute pancreatitis patients using interpretable machine learning algorithms

Chunying Liu, Binglin Song, Kangrui Fu, Ping Liu
4 citations
Digital Health
Pancreatitis Pathology and Treatment
8.03
article

Developing a predictive model for septic shock risk in acute pancreatitis patients using interpretable machine learning algorithms

Chunying Liu, Binglin Song, Kangrui Fu, Ping Liu
article en
4 citations

Abstract

ML approaches exhibited promising performance in predicting septic shock in patients with AP. These models may aid in guiding treatment decisions for patients with AP in the emergency department.

Digital HealthVol. 11
North Sichuan Medical University (CN), Dazhou Central Hospital (CN)
Openalex Percentile: Top 3%
Pancreatitis Pathology and Treatment
8.03
DOIOpen Access PDFOpenAlex
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