Information Extraction From Electronic Health Records to Predict Readmission Following Acute Myocardial Infarction: Does Natural Language Processing Using Clinical Notes Improve Prediction of Readmission?

Information Extraction From Electronic Health Records to Predict Readmission Following Acute Myocardial Infarction: Does Natural Language Processing Using Clinical Notes Improve Prediction of Readmission?

Background Social risk factors influence rehospitalization rates yet are challenging to incorporate into prediction models. Integration of social risk factors using natural language processing (NLP) and machine learning could improve risk prediction of 30‐day readmission following an acute myocardia...

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Bibliographic Details
Main Authors: Jeremiah R. Brown, Iben M. Ricket, Ruth M. Reeves, Rashmee U. Shah, Christine A. Goodrich, Glen Gobbel, Meagan E. Stabler, Amy M. Perkins, Freneka Minter, Kevin C. Cox, Chad Dorn, Jason Denton, Bruce E. Bray, Ramkiran Gouripeddi, John Higgins, Wendy W. Chapman, Todd MacKenzie, Michael E. Matheny
Format: Article
Language:English
Published: Wiley 2022-04-01
Series:Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
Subjects:
electronic health records
machine learning
myocardial infarction
natural language processing
patient readmission
Online Access:https://www.ahajournals.org/doi/10.1161/JAHA.121.024198

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https://www.ahajournals.org/doi/10.1161/JAHA.121.024198

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