Personalized Risk Prediction for 30-Day Readmissions With Venous Thromboembolism Using Machine Learning.

Journal: Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing

Published Date: May 1, 2021

Abstract

PURPOSE: The aim of the study was to develop and validate machine learning models to predict the personalized risk for 30-day readmission with venous thromboembolism (VTE).

Authors

Jung In Park

Department of Medicine, Center for Biomedical Informatics Research, Stanford University, Stanford, CA.
Doyub Kim

Principle Software Engineer, NVIDIA, Santa Clara, CA.
Jung-Ah Lee

Associate Professor, Sue & Bill Gross School of Nursing, University of California, Irvine, CA.
Kai Zheng

University of California, Irvine, Irvine, CA, USA.
Alpesh Amin

Professor, School of Medicine, University of California, Irvine, CA.

Keywords

Academic Medical Centers Adult Aged Electronic Health Records Female Humans Machine Learning Male Middle Aged Models, Statistical Patient Readmission Retrospective Studies Risk Assessment Venous Thromboembolism

External Resources

View on PubMed Access via DOI PubMed (33617689)

Personalized Risk Prediction for 30-Day Readmissions With Venous Thromboembolism Using Machine Learning.

Abstract

Authors

Keywords

External Resources

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