Using machine learning to develop smart reflex testing protocols.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 37812770

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.

Authors

  • Matthew McDermott
    MIT Computer Science and Artificial Intelligence Lab, Boston, Massachusetts.
  • Anand Dighe
    Department of Pathology, Massachusetts General Hospital, Boston, MA 02114, United States.
  • Peter Szolovits
    Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Yuan Luo
    Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL 60611, USA.
  • Jason Baron
    Department of Pathology, Massachusetts General Hospital, Boston, MA 02114, United States.

Keywords

External Resources

Popular Topics
Recent Journals