Utilizing electronic health data and machine learning for the prediction of 30-day unplanned readmission or all-cause mortality in heart failure

Utilizing electronic health data and machine learning for the prediction of 30-day unplanned readmission or all-cause mortality in heart failure

Background: Existing risk assessment tools for heart failure (HF) outcomes use structured databases with static, single-timepoint clinical data and have limited accuracy. Objective: The purpose of this study was to develop a comprehensive approach for accurate prediction of 30-day unplanned readmiss...

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Bibliographic Details
Main Authors: Ashley N. Beecy, MD, Manasa Gummalla, BA, Evan Sholle, MS, Zhuoran Xu, MSc, Yiye Zhang, MSc, PhD, Kelly Michalak, BA, Kristina Dolan, BA, Yasin Hussain, MD, Benjamin C. Lee, PhD, Yongkang Zhang, PhD, Parag Goyal, MSc, MD, Thomas R. Campion, Jr., PhD, Leslee J. Shaw, PhD, Lohendran Baskaran, MBBS, Subhi J. Al’Aref, MD
Format: Article
Language:English
Published: Elsevier 2020-09-01
Series:Cardiovascular Digital Health Journal
Subjects:
Big data
Electronic health data
Heart failure
Machine learning
Readmission
Online Access:http://www.sciencedirect.com/science/article/pii/S2666693620300104

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