This paper is in the following e-collection/theme issue:

Artificial Intelligence (2146) Decision Support for Health Professionals (1550) Clinical Information and Decision Making (1930) Machine Learning (2085) Recruitment of Research Participants (379) Intensive Care Unit (ICU) (160)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/54621, first published .
Enhancing Patient Selection in Sepsis Clinical Trials Design Through an AI Enrichment Strategy: Algorithm Development and Validation

Enhancing Patient Selection in Sepsis Clinical Trials Design Through an AI Enrichment Strategy: Algorithm Development and Validation

Enhancing Patient Selection in Sepsis Clinical Trials Design Through an AI Enrichment Strategy: Algorithm Development and Validation

Authors of this article:

Meicheng Yang1 Author Orcid Image ;   Jinqiang Zhuang2, 3 Author Orcid Image ;   Wenhan Hu4 Author Orcid Image ;   Jianqing Li1, 5 Author Orcid Image ;   Yu Wang3 Author Orcid Image ;   Zhongheng Zhang6 Author Orcid Image ;   Chengyu Liu1 Author Orcid Image ;   Hui Chen4 Author Orcid Image
Article Authors Cited by (4) Tweetations Metrics

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  1. Ma X, Mai Y, Ma Y, Ma X. Constructing an early warning model for elderly sepsis patients based on machine learning. Scientific Reports 2025;15(1) View
  2. Du K, Xiong H, Zhang X, Luo R, Zhou B. Bioinformatics analysis to identify environmental endocrine chemicals that target endometriosis genes. Ecotoxicology and Environmental Safety 2025;295:118136 View
  3. Sasidharan S, Dhillon H. Emerging Paradigms in Sepsis Management: Critical Insights from Contemporary Clinical Trials. APIK Journal of Internal Medicine 2025;13(4):238 View
  4. Cui C, Zhao J, Mu F, Wang D, Li Y, Tang M, Sun K, Gong R, Yan Z, Wang J. Interpretable machine learning based comorbidity specific mortality risk score for bloodstream infections. Journal of Infection 2025;91(6):106656 View