Utilizing electronic health data and machine learning for the prediction of 30-day unplanned readmission or all-cause mortality in heart failure

BACKGROUND: Existing risk assessment tools for heart failure (HF) outcomes use structured databases with static, single-timepoint clinical data and have limited accuracy. OBJECTIVE: The purpose of this study was to develop a comprehensive approach for accurate prediction of 30-day unplanned readmiss...

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Detalles Bibliográficos
Autores principales: Beecy, Ashley N., Gummalla, Manasa, Sholle, Evan, Xu, Zhuoran, Zhang, Yiye, Michalak, Kelly, Dolan, Kristina, Hussain, Yasin, Lee, Benjamin C., Zhang, Yongkang, Goyal, Parag, Campion, Thomas R., Shaw, Leslee J., Baskaran, Lohendran, Al’Aref, Subhi J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Clinical
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8890080/
https://www.ncbi.nlm.nih.gov/pubmed/35265878
http://dx.doi.org/10.1016/j.cvdhj.2020.07.004

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8890080/
https://www.ncbi.nlm.nih.gov/pubmed/35265878
http://dx.doi.org/10.1016/j.cvdhj.2020.07.004

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