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...

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Detalles Bibliográficos
Autores principales: Fernández-Gutiérrez, Fabiola, Kennedy, Jonathan I., Cooksey, Roxanne, Atkinson, Mark, Choy, Ernest, Brophy, Sinead, Huo, Lin, Zhou, Shang-Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8534858/
https://www.ncbi.nlm.nih.gov/pubmed/34679609
http://dx.doi.org/10.3390/diagnostics11101908

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8534858/
https://www.ncbi.nlm.nih.gov/pubmed/34679609
http://dx.doi.org/10.3390/diagnostics11101908

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