Development and validation of phenotype classifiers across multiple sites in the observational health data sciences and informatics network

OBJECTIVE: Accurate electronic phenotyping is essential to support collaborative observational research. Supervised machine learning methods can be used to train phenotype classifiers in a high-throughput manner using imperfectly labeled data. We developed 10 phenotype classifiers using this approac...

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Detalles Bibliográficos
Autores principales: Kashyap, Mehr, Seneviratne, Martin, Banda, Juan M, Falconer, Thomas, Ryu, Borim, Yoo, Sooyoung, Hripcsak, George, Shah, Nigam H
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Research and Applications
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7309227/
https://www.ncbi.nlm.nih.gov/pubmed/32374408
http://dx.doi.org/10.1093/jamia/ocaa032

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7309227/
https://www.ncbi.nlm.nih.gov/pubmed/32374408
http://dx.doi.org/10.1093/jamia/ocaa032

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