一种机器学习决策支持工具可优化新生儿重症监护病房中全基因组测序（WGS）的利用。
A machine learning decision support tool optimizes WGS utilization in a neonatal intensive care unit.
作者信息
Juarez Edwin F, Peterson Bennet, Sanford Kobayashi Erica, Gilmer Sheldon, Tobin Laura E, Schultz Brandan, Lenberg Jerica, Carroll Jeanne, Bai-Tong Shiyu, Sweeney Nathaly M, Beebe Curtis, Stewart Lawrence, Olsen Lauren, Reinke Julie, Kiernan Elizabeth A, Reimers Rebecca, Wigby Kristen, Tackaberry Chris, Yandell Mark, Hobbs Charlotte, Bainbridge Matthew N
机构信息
Rady Children's Institute for Genomic Medicine, San Diego, CA, USA.
Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
出版信息
NPJ Digit Med. 2025 Jan 30;8(1):72. doi: 10.1038/s41746-025-01458-9.
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.
孟德尔表型搜索引擎（MPSE）是一种使用自然语言处理和机器学习的临床决策支持工具，它帮助新生儿科医生加快了对全基因组测序（WGS）的决策，以便在新生儿重症监护病房诊断患者。引入MPSE后，WGS的使用率提高，订购WGS的时间缩短，WGS的诊断率提高。
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