Applying active learning to high-throughput phenotyping algorithms for electronic health records data

OBJECTIVES: Generalizable, high-throughput phenotyping methods based on supervised machine learning (ML) algorithms could significantly accelerate the use of electronic health records data for clinical and translational research. However, they often require large numbers of annotated samples, which...

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Detalhes bibliográficos
Main Authors: Chen, Yukun, Carroll, Robert J, Hinz, Eugenia R McPeek, Shah, Anushi, Eyler, Anne E, Denny, Joshua C, Xu, Hua
Formato: Artigo
Idioma:Inglês
Publicado em: BMJ Publishing Group 2013
Assuntos:
Research and Applications
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC3861916/
https://ncbi.nlm.nih.gov/pubmed/23851443
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1136/amiajnl-2013-001945
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