Interpretable Machine Learning for Early Prediction of Prognosis in Sepsis: A Discovery and Validation Study

INTRODUCTION: This study aimed to develop and validate an interpretable machine-learning model based on clinical features for early predicting in-hospital mortality in critically ill patients with sepsis. METHODS: We enrolled all patients with sepsis in the Medical Information Mart for Intensive Car...

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Detalles Bibliográficos
Autores principales: Hu, Chang, Li, Lu, Huang, Weipeng, Wu, Tong, Xu, Qiancheng, Liu, Juan, Hu, Bo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Healthcare 2022
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9124279/
https://www.ncbi.nlm.nih.gov/pubmed/35399146
http://dx.doi.org/10.1007/s40121-022-00628-6

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9124279/
https://www.ncbi.nlm.nih.gov/pubmed/35399146
http://dx.doi.org/10.1007/s40121-022-00628-6

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