Cargando…
Explainable machine learning to predict long-term mortality in critically ill ventilated patients: a retrospective study in central Taiwan
BACKGROUND: Machine learning (ML) model is increasingly used to predict short-term outcome in critically ill patients, but the study for long-term outcome is sparse. We used explainable ML approach to establish 30-day, 90-day and 1-year mortality prediction model in critically ill ventilated patient...
Autores principales: | Chan, Ming-Cheng, Pai, Kai-Chih, Su, Shao-An, Wang, Min-Shian, Wu, Chieh-Liang, Chao, Wen-Cheng |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8953968/ https://www.ncbi.nlm.nih.gov/pubmed/35337303 http://dx.doi.org/10.1186/s12911-022-01817-6 |
Ejemplares similares
-
Explainable machine learning approach to predict extubation in critically ill ventilated patients: a retrospective study in central Taiwan
por: Pai, Kai-Chih, et al.
Publicado: (2022) -
Impact of Early Fluid Balance on Long-Term Mortality in Critically Ill Surgical Patients: A Retrospective Cohort Study in Central Taiwan
por: Wu, Chieh-Liang, et al.
Publicado: (2021) -
Explainable Machine Learning to Predict Successful Weaning Among Patients Requiring Prolonged Mechanical Ventilation: A Retrospective Cohort Study in Central Taiwan
por: Lin, Ming-Yen, et al.
Publicado: (2021) -
Explainable Machine Learning to Predict Successful Weaning of Mechanical Ventilation in Critically Ill Patients Requiring Hemodialysis
por: Lin, Ming-Yen, et al.
Publicado: (2023) -
Federated machine learning for predicting acute kidney injury in critically ill patients: a multicenter study in Taiwan
por: Huang, Chun-Te, et al.
Publicado: (2023)