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

被引：0
|
作者：
Hossein Estiri
Jeffrey G. Klann
Shawn N. Murphy
机构：
[1] Massachusetts General Hospital,Laboratory of Computer Science
[2] Partners HealthCare,Research Information Science and Computing
[3] Massachusetts General Hospital,Department of Neurology
来源：
BMC Medical Informatics and Decision Making | / 19卷
关键词：
Unsupervised clustering; Implausible observations; Data quality; Electronic health records; Informatics applications; Anomaly detection;
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