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

Development and Validation of Machine Learning Models for Prediction of 1-Year Mortality... 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 1 2 1 Nishant Sahni, MD, MS , Gyorgy Simon, PhD , and Rashi Arora, MD 1 2 Division of General Internal Medicine, University of Minnesota, Minneapolis, MN, USA; Institute of Health Informatics, University of Minnesota, Minneapolis, MN, USA. EOL End of life BACKGROUND: Predicting death in a cohort of clinically EOLp End of life planning diverse, multicondition hospitalized patients is difficult. EMR Electronic medical record Prognostic models that use electronic medical record MCV Mean corpuscular volume (EMR) data to determine 1-year death risk can improve WBC White blood cell count end-of-life planning and risk adjustment for research. CMP Complete metabolic panel OBJECTIVE: Determine if the final set of demographic, CBC Complete blood count vital sign, and laboratory data from a hospitalization can BMP Basic metabolic panel be used to accurately quantify 1-year mortality risk. RF DESIGN: A retrospective study using electronic medical Random forest LR Likelihood ratio record data linked with the state death registry. LR Logistic regression PARTICIPANTS: A total of 59,848 hospitalized patients SD Standard deviation within http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of General Internal Medicine Springer Journals

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

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Publisher
Springer US
Copyright
Copyright © 2018 by Society of General Internal Medicine
Subject
Medicine & Public Health; Internal Medicine
ISSN
0884-8734
eISSN
1525-1497
D.O.I.
10.1007/s11606-018-4316-y
Publisher site
See Article on Publisher Site

Abstract

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 1 2 1 Nishant Sahni, MD, MS , Gyorgy Simon, PhD , and Rashi Arora, MD 1 2 Division of General Internal Medicine, University of Minnesota, Minneapolis, MN, USA; Institute of Health Informatics, University of Minnesota, Minneapolis, MN, USA. EOL End of life BACKGROUND: Predicting death in a cohort of clinically EOLp End of life planning diverse, multicondition hospitalized patients is difficult. EMR Electronic medical record Prognostic models that use electronic medical record MCV Mean corpuscular volume (EMR) data to determine 1-year death risk can improve WBC White blood cell count end-of-life planning and risk adjustment for research. CMP Complete metabolic panel OBJECTIVE: Determine if the final set of demographic, CBC Complete blood count vital sign, and laboratory data from a hospitalization can BMP Basic metabolic panel be used to accurately quantify 1-year mortality risk. RF DESIGN: A retrospective study using electronic medical Random forest LR Likelihood ratio record data linked with the state death registry. LR Logistic regression PARTICIPANTS: A total of 59,848 hospitalized patients SD Standard deviation within

Journal

Journal of General Internal MedicineSpringer Journals

Published: Jan 30, 2018

References

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