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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...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2019
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| 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 |