Using machine learning to develop smart reflex testing protocols.

ABSTRACT:

Objective

Reflex testing protocols allow clinical laboratories to perform second line diagnostic tests on existing specimens based on the results of initially ordered tests. Reflex testing can support optimal clinical laboratory test ordering and diagnosis. In current clinical practice, reflex testing typically relies on simple "if-then" rules; however, this limits the opportunities for reflex testing since most test ordering decisions involve more complexity than traditional rule-based approaches would allow. Here, using the analyte ferritin as an example, we propose an alternative machine learning-based approach to "smart" reflex testing.

Methods

Using deidentified patient data, we developed a machine learning model to predict whether a patient getting CBC testing will als

SUBMITTER: McDermott M 

PROVIDER: S-EPMC10797267 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature