A clustering approach for detecting implausible observation values in electronic health records data

BACKGROUND: Identifying implausible clinical observations (e.g., laboratory test and vital sign values) in Electronic Health Record (EHR) data using rule-based procedures is challenging. Anomaly/outlier detection methods can be applied as an alternative algorithmic approach to flagging such implausi...

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Detalles Bibliográficos
Autores principales: Estiri, Hossein, Klann, Jeffrey G., Murphy, Shawn N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Technical Advance
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6652024/
https://www.ncbi.nlm.nih.gov/pubmed/31337390
http://dx.doi.org/10.1186/s12911-019-0852-6

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6652024/
https://www.ncbi.nlm.nih.gov/pubmed/31337390
http://dx.doi.org/10.1186/s12911-019-0852-6

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