Mining Primary Care Electronic Health Records for Automatic Disease Phenotyping: A Transparent Machine Learning Framework

Mining Primary Care Electronic Health Records for Automatic Disease Phenotyping: A Transparent Machine Learning Framework

(1) Background: We aimed to develop a transparent machine-learning (ML) framework to automatically identify patients with a condition from electronic health records (EHRs) via a parsimonious set of features. (2) Methods: We linked multiple sources of EHRs, including 917,496,869 primary care records...

Full description

Bibliographic Details
Main Authors: Fabiola Fernández-Gutiérrez, Jonathan I. Kennedy, Roxanne Cooksey, Mark Atkinson, Ernest Choy, Sinead Brophy, Lin Huo, Shang-Ming Zhou
Format: Article
Language:English
Published: MDPI AG 2021-10-01
Series:Diagnostics
Subjects:
phenotyping
rheumatology
cohort identification
electronic health records
feature selection
transparent machine learning
Online Access:https://www.mdpi.com/2075-4418/11/10/1908

Internet

https://www.mdpi.com/2075-4418/11/10/1908

Similar Items