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

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

Abstract 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...

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Bibliographic Details
Main Authors: Hossein Estiri, Jeffrey G. Klann, Shawn N. Murphy
Format: Article
Language:English
Published: BMC 2019-07-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Unsupervised clustering
Implausible observations
Data quality
Electronic health records
Informatics applications
Anomaly detection
Online Access:http://link.springer.com/article/10.1186/s12911-019-0852-6

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http://link.springer.com/article/10.1186/s12911-019-0852-6

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