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

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

Abstract 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 comple...

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Bibliographic Details
Main Authors: Sara Bersche Golas, Takuma Shibahara, Stephen Agboola, Hiroko Otaki, Jumpei Sato, Tatsuya Nakae, Toru Hisamitsu, Go Kojima, Jennifer Felsted, Sujay Kakarmath, Joseph Kvedar, Kamal Jethwani
Format: Article
Language:English
Published: BMC 2018-06-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Heart failure
Machine learning
Deep learning
Deep unified networks
Readmission reduction
Value-based care
Online Access:http://link.springer.com/article/10.1186/s12911-018-0620-z

Internet

http://link.springer.com/article/10.1186/s12911-018-0620-z

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