Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data

OBJECTIVE: In applying machine learning (ML) to electronic health record (EHR) data, many decisions must be made before any ML is applied; such preprocessing requires substantial effort and can be labor-intensive. As the role of ML in health care grows, there is an increasing need for systematic and...

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Detalles Bibliográficos
Autores principales: Tang, Shengpu, Davarmanesh, Parmida, Song, Yanmeng, Koutra, Danai, Sjoding, Michael W, Wiens, Jenna
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/PMC7727385/
https://www.ncbi.nlm.nih.gov/pubmed/33040151
http://dx.doi.org/10.1093/jamia/ocaa139

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7727385/
https://www.ncbi.nlm.nih.gov/pubmed/33040151
http://dx.doi.org/10.1093/jamia/ocaa139

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