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

Sepsis is a heterogeneous syndrome, and enrollment of more homogeneous patients is essential to improve the efficiency of clinical trials. Artificial intelligence (AI) has facilitated the identification of homogeneous subgroups, but how to estimate the uncertainty of the model outputs when applying...

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Veröffentlicht in:	Journal of medical Internet research Jg. 26; H. 8; S. e54621
Hauptverfasser:	Yang, Meicheng, Zhuang, Jinqiang, Hu, Wenhan, Li, Jianqing, Wang, Yu, Zhang, Zhongheng, Liu, Chengyu, Chen, Hui
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Canada Journal of Medical Internet Research 04.09.2024 JMIR Publications
Schlagworte:	Aged Algorithms Artificial Intelligence Clinical trials Clinical Trials as Topic - methods Data mining Decision-making Female Humans Infection Machine learning Male Medical centers Medical research Medicine, Experimental Middle Aged Online databases Original Paper Patient Selection Retrospective Studies Sepsis - therapy disease progression trajectories conformal prediction predictive modeling artificial intelligence enrichment strategy sepsis
ISSN:	1438-8871, 1439-4456, 1438-8871
Online-Zugang:	Volltext
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