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 their scope since most test ordering decisions involve more complexity than a simple rule will allow. Here, using the analyte ferritin as an example, we propose an alternative machine learning-based approach to "smart" reflex testing with a wider scope and greater impact than traditional rule-based approaches.

Methods

Using patient data, we developed a machine learning model to predict whether a patient getting CB

SUBMITTER: McDermott M 

PROVIDER: S-EPMC9915755 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature