Semi-supervised encoding for outlier detection in clinical observation data.

ABSTRACT:

Background and objective

Electronic Health Record (EHR) data often include observation records that are unlikely to represent the "truth" about a patient at a given clinical encounter. Due to their high throughput, examples of such implausible observations are frequent in records of laboratory test results and vital signs. Outlier detection methods can offer low-cost solutions to flagging implausible EHR observations. This article evaluates the utility of a semi-supervised encoding approach (super-encoding) for constructing non-linear exemplar data distributions from EHR observation data and detecting non-conforming observations as outliers.

Methods

Two hypotheses are tested using experimental design and non-parametric hypothesis testing procedures: (1) adding demographic fe

SUBMITTER: Estiri H 

PROVIDER: S-EPMC10111961 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature