Open AccessElectronic phenotyping of health outcomes of interest using a linked claims-electronic health record database: Findings from a machine learning pilot project
Author(s) -
Teresa B. Gibson,
Michael Nguyen,
Timothy Burrell,
Frank Yoon,
Jenna Wong,
Sai Dharmarajan,
Rita OuelletHellstrom,
Wei Hua,
Yong Ma,
Elande Baro,
Sarah Bloemers,
Cory Pack,
Adee Kennedy,
Sengwee Toh,
Robert Ball
Publication year - 2021
Publication title -
journal of the american medical informatics association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.614
H-Index - 150
eISSN - 1527-974X
pISSN - 1067-5027
DOI - 10.1093/jamia/ocab036
Subject(s) - machine learning , random forest , artificial intelligence , receiver operating characteristic , lasso (programming language) , logistic regression , ensemble learning , electronic health record , computer science , support vector machine , medicine , ensemble forecasting , data mining , health care , economics , economic growth , world wide web
Claims-based algorithms are used in the Food and Drug Administration Sentinel Active Risk Identification and Analysis System to identify occurrences of health outcomes of interest (HOIs) for medical product safety assessment. This project aimed to apply machine learning classification techniques to demonstrate the feasibility of developing a claims-based algorithm to predict an HOI in structured electronic health record (EHR) data.