Making work visible for electronic phenotype implementation: lessons learned from the eMERGE network

BACKGROUND: Implementation of phenotype algorithms requires phenotype engineers to interpret human-readable algorithms and translate the description (text and flowcharts) into computable phenotypes – a process that can be labor intensive and error prone. To address the critical need for reducing the...

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Detalhes bibliográficos
Publicado no:J Biomed Inform
Main Authors: Shang, Ning, Liu, Cong, Rasmussen, Luke V., Ta, Casey N., Caroll, Robert J., Benoit, Barbara, Lingren, Todd, Dikilitas, Ozan, Mentch, Frank D., Carrell, David S., Wei, Wei-Qi, Luo, Yuan, Gainer, Vivian S., Kullo, Iftikhar J., Pacheco, Jennifer A., Hakonarson, Hakon, Walunas, Theresa L., Denny, Joshua C., Wiley, Ken, Murphy, Shawn N., Hripcsak, George, Weng, Chunhua
Formato: Artigo
Idioma:Inglês
Publicado em: 2019
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Article
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC6894517/
https://ncbi.nlm.nih.gov/pubmed/31542521
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.jbi.2019.103293
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