Development and Validation of Machine Learning Models for Prediction of 1-Year Mortality Utilizing Electronic Medical Record Data Available at the End of Hospitalization in Multicondition Patients: a Proof-of-Concept Study.

Journal: Journal of general internal medicine

Published Date: Jun 1, 2018

Abstract

BACKGROUND: Predicting death in a cohort of clinically diverse, multicondition hospitalized patients is difficult. Prognostic models that use electronic medical record (EMR) data to determine 1-year death risk can improve end-of-life planning and risk adjustment for research.

Authors

Nishant Sahni

Division of General Internal Medicine, University of Minnesota, Minneapolis, MN, USA. nishant.sahni@gmail.com.
Gyorgy Simon

University of Minnesota, Institute for Health Informatics.
Rashi Arora

Division of General Internal Medicine, University of Minnesota, Minneapolis, MN, USA.

Keywords

Adult Aged Aged, 80 and over Cohort Studies Data Analysis Electronic Health Records Female Forecasting Hospitalization Humans Machine Learning Male Middle Aged Models, Theoretical Mortality Proof of Concept Study Reproducibility of Results Retrospective Studies Risk Factors

External Resources

View on PubMed Access via DOI PubMed (29383551)

Development and Validation of Machine Learning Models for Prediction of 1-Year Mortality Utilizing Electronic Medical Record Data Available at the End of Hospitalization in Multicondition Patients: a Proof-of-Concept Study.

Abstract

Authors

Keywords

External Resources

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