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 implausible values in EHRs.

Methods

The primary objectives of this research were to develop and test an unsupervised clustering-based anomaly/outlier detection approach for detecting implausible observations in EHR data as an alternative algorithmic solution to the existing procedures. Our approach is built upon two underlying hypotheses that, (i) when there are large number of observations, implausible records should be sparse, and therefore (ii) if these data are clustered properly, clus

SUBMITTER: Estiri H 

PROVIDER: S-EPMC6652024 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature