Cargando…
A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data
BACKGROUND: Heart failure is one of the leading causes of hospitalization in the United States. Advances in big data solutions allow for storage, management, and mining of large volumes of structured and semi-structured data, such as complex healthcare data. Applying these advances to complex health...
Autores principales: | Golas, Sara Bersche, Shibahara, Takuma, Agboola, Stephen, Otaki, Hiroko, Sato, Jumpei, Nakae, Tatsuya, Hisamitsu, Toru, Kojima, Go, Felsted, Jennifer, Kakarmath, Sujay, Kvedar, Joseph, Jethwani, Kamal |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6013959/ https://www.ncbi.nlm.nih.gov/pubmed/29929496 http://dx.doi.org/10.1186/s12911-018-0620-z |
Ejemplares similares
-
Validating a Machine Learning Algorithm to Predict 30-Day Re-Admissions in Patients With Heart Failure: Protocol for a Prospective Cohort Study
por: Kakarmath, Sujay, et al.
Publicado: (2018) -
Predictive Modeling of 30-Day Emergency Hospital Transport of Patients Using a Personal Emergency Response System: Prognostic Retrospective Study
por: op den Buijs, Jorn, et al.
Publicado: (2018) -
Assessing the Usability of an Automated Continuous Temperature Monitoring Device (iThermonitor) in Pediatric Patients: Non-Randomized Pilot Study
por: Kakarmath, Sujay S, et al.
Publicado: (2018) -
Factors Influencing Exercise Engagement When Using Activity Trackers: Nonrandomized Pilot Study
por: Centi, Amanda Jayne, et al.
Publicado: (2019) -
Health Care Cost Analyses for Exploring Cost Savings Opportunities in Older Patients: Longitudinal Retrospective Study
por: Agboola, Stephen, et al.
Publicado: (2018)