Peer-Reviewed Publication
NPJ Digit Med2025;8(1):72.January 30, 2025Journal Article
A machine learning decision support tool optimizes WGS utilization in a neonatal intensive care unit.
Edwin F Juarez1, Bennet Peterson2, Erica Sanford Kobayashi3,4, Sheldon Gilmer5, Laura E Tobin3, Brandan Schultz3, Jerica Lenberg3, Jeanne Carroll5,6, Shiyu Bai-Tong5,6, Nathaly M Sweeney5,6, Curtis Beebe5, Lawrence Stewart5, Lauren Olsen5, Julie Reinke5, Elizabeth A Kiernan5, Rebecca Reimers3,6,7, Kristen Wigby3,8, Chris Tackaberry9, Mark Yandell10, Charlotte Hobbs3, Matthew N Bainbridge3
1Rady Children's Institute for Genomic Medicine, San Diego, CA, USA. ejuarezrosales@rchsd.org.
2Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
3Rady Children's Institute for Genomic Medicine, San Diego, CA, USA.
4Department of Pediatrics, Division of Critical Care Medicine, Children's Hospital Orange County, Orange, CA, USA.
5Rady Children's Hospital San Diego, San Diego, CA, USA.
6University of California, San Diego, La Jolla, CA, USA.
7Scripps Research Translational Institute, La Jolla, CA, USA.
8Department of Pediatrics, University of California, Davis, Sacramento, CA, USA.
9Clinithink, London, UK.
10Department of Human Genetics, Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, USA.
Abstract
The Mendelian Phenotype Search Engine (MPSE), a clinical decision support tool using Natural Language Processing and Machine Learning, helped neonatologists expedite decisions to whole genome sequencing (WGS) to diagnose patients in the neonatal intensive care unit. After the MPSE was introduced, utilization of WGS increased, time to ordering WGS decreased, and WGS diagnostic yield increased.
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