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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.

DOI:10.1038/s41746-025-01458-9
PMID:39885315
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11782664/
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.

摘要

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。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f6fa/11782664/98b002797900/41746_2025_1458_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f6fa/11782664/83ad4e0fcf79/41746_2025_1458_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f6fa/11782664/98b002797900/41746_2025_1458_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f6fa/11782664/83ad4e0fcf79/41746_2025_1458_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f6fa/11782664/98b002797900/41746_2025_1458_Fig2_HTML.jpg

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本文引用的文献

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The Human Phenotype Ontology in 2024: phenotypes around the world.2024 年人类表型本体：世界各地的表型。
Nucleic Acids Res. 2024 Jan 5;52(D1):D1333-D1346. doi: 10.1093/nar/gkad1005.
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The Alarm Fatigue Challenge in the Neonatal Intensive Care Unit: A "before" and "after" Study.新生儿重症监护病房中的报警疲劳挑战：一项“前后”研究。
Am J Perinatol. 2024 May;41(S 01):e2348-e2355. doi: 10.1055/a-2113-8364. Epub 2023 Jun 20.
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Enriching representation learning using 53 million patient notes through human phenotype ontology embedding.通过人类表型本体嵌入使用 5300 万患者笔记来丰富表示学习。
Artif Intell Med. 2023 May;139:102523. doi: 10.1016/j.artmed.2023.102523. Epub 2023 Feb 28.
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