Machine learning models for predicting in-hospital mortality in patient with sepsis: Analysis of vital sign dynamics

PURPOSE: To build machine learning models for predicting the risk of in-hospital death in patients with sepsis within 48 h, using only dynamic changes in the patient's vital signs. METHODS: This retrospective observational cohort study enrolled septic patients from five emergency departments (E...

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Detalles Bibliográficos
Autores principales: Cheng, Chi-Yung, Kung, Chia-Te, Chen, Fu-Cheng, Chiu, I-Min, Lin, Chun-Hung Richard, Chu, Chun-Chieh, Kung, Chien Feng, Su, Chih-Min
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Medicine
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9631306/
https://www.ncbi.nlm.nih.gov/pubmed/36341257
http://dx.doi.org/10.3389/fmed.2022.964667

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9631306/
https://www.ncbi.nlm.nih.gov/pubmed/36341257
http://dx.doi.org/10.3389/fmed.2022.964667

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