Early prediction of sepsis-induced coagulopathy in the ICU using interpretable machine learning: a multi-center retrospective cohort study

This model demonstrated strong predictive ability and clinical interpretability, enabling early <i>SIC</i> identification and targeted intervention.

Authors

Hao Jiang (ORCID: https://orcid.org/0000-0001-6888-7742)
Lei Feng (ORCID: https://orcid.org/0000-0001-6053-8417)
Sha Tao (ORCID: https://orcid.org/0000-0002-0928-0681)

Institutions

Huadong Hospital (CN)

Publication Details

Journal: Frontiers in Medicine
Published: 2025-11-05
DOI: https://doi.org/10.3389/fmed.2025.1681621
Primary Topic: Sepsis Diagnosis and Treatment
Type: article
Field-Weighted Citation Impact: 0.00

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article

Early prediction of sepsis-induced coagulopathy in the ICU using interpretable machine learning: a multi-center retrospective cohort study

Hao Jiang, Lei Feng, Sha Tao

Nov 5, 2025

Frontiers in Medicine

Sepsis Diagnosis and Treatment

article

Early prediction of sepsis-induced coagulopathy in the ICU using interpretable machine learning: a multi-center retrospective cohort study

Hao Jiang, Lei Feng, Sha Tao

November 5, 2025article en

Abstract

This model demonstrated strong predictive ability and clinical interpretability, enabling early <i>SIC</i> identification and targeted intervention.

Frontiers in MedicineVol. 12

Huadong Hospital (CN)

Openalex Percentile: Top 57%

Sepsis Diagnosis and Treatment

DOI Open Access PDF OpenAlex

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Early prediction of sepsis-induced coagulopathy in the ICU using interpretable machine learning: a multi-center retrospective cohort study — Hao Jiang, Lei Feng, et al. · Frontiers in Medicine (2025) | TGRS Research Map | TGRS