Cargando…
The Price of Explainability in Machine Learning Models for 100-Day Readmission Prediction in Heart Failure: Retrospective, Comparative, Machine Learning Study
BACKGROUND: Sensitive and interpretable machine learning (ML) models can provide valuable assistance to clinicians in managing patients with heart failure (HF) at discharge by identifying individual factors associated with a high risk of readmission. In this cohort study, we delve into the factors d...
Autores principales: | Soliman, Amira, Agvall, Björn, Etminani, Kobra, Hamed, Omar, Lingman, Markus |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10638630/ https://www.ncbi.nlm.nih.gov/pubmed/37889530 http://dx.doi.org/10.2196/46934 |
Ejemplares similares
-
Clinical characteristics at hospital discharge that predict cardiovascular readmission within 100 days in heart failure patients – An observational study
por: Davidge, Jason, et al.
Publicado: (2023) -
Characteristics, management and outcomes in patients with CKD in a healthcare region in Sweden: a population-based, observational study
por: Agvall, Björn, et al.
Publicado: (2023) -
Machine learning for predicting readmission risk among the frail: Explainable AI for healthcare
por: Mohanty, Somya D., et al.
Publicado: (2021) -
Machine Learning-Enabled 30-Day Readmission Model for Stroke Patients
por: Darabi, Negar, et al.
Publicado: (2021) -
Explainable machine learning models for predicting 30-day readmission in pediatric pulmonary hypertension: A multicenter, retrospective study
por: Duan, Minjie, et al.
Publicado: (2022)