一种机器学习决策支持工具优化了新生儿重症监护病房中全基因组测序的应用。 - Suppr

A Machine Learning Decision Support Tool Optimizes Whole Genome Sequencing Utilization in a Neonatal Intensive Care Unit.

作者信息

Juarez Edwin F, Peterson Bennet, Kobayashi Erica Sanford, 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

出版信息

medRxiv. 2024 Jul 7:2024.07.05.24310008. doi: 10.1101/2024.07.05.24310008.

DOI:10.1101/2024.07.05.24310008

PMID:39006422

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11245077/

Abstract

摘要

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A Machine Learning Decision Support Tool Optimizes Whole Genome Sequencing Utilization in a Neonatal Intensive Care Unit.一种机器学习决策支持工具优化了新生儿重症监护病房中全基因组测序的应用。

medRxiv. 2024 Jul 7:2024.07.05.24310008. doi: 10.1101/2024.07.05.24310008.

A machine learning decision support tool optimizes WGS utilization in a neonatal intensive care unit.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。

NPJ Digit Med. 2025 Jan 30;8(1):72. doi: 10.1038/s41746-025-01458-9.

Estimation of Caffeine Regimens: A Machine Learning Approach for Enhanced Clinical Decision Making at a Neonatal Intensive Care Unit (NICU).咖啡因给药方案的评估：一种用于加强新生儿重症监护病房（NICU）临床决策的机器学习方法。

Crit Rev Biomed Eng. 2018;46(2):93-115. doi: 10.1615/CritRevBiomedEng.2018025933.

Improving Prediction of Survival for Extremely Premature Infants Born at 23 to 29 Weeks Gestational Age in the Neonatal Intensive Care Unit: Development and Evaluation of Machine Learning Models.改善新生儿重症监护病房中孕23至29周出生的极早产儿生存预测：机器学习模型的开发与评估

JMIR Med Inform. 2024 Feb 14;12:e42271. doi: 10.2196/42271.

State of the Art of Machine Learning-Enabled Clinical Decision Support in Intensive Care Units: Literature Review.重症监护病房中基于机器学习的临床决策支持技术现状：文献综述

JMIR Med Inform. 2022 Mar 3;10(3):e28781. doi: 10.2196/28781.

Utilization of Decision Tree Algorithms for Supporting the Prediction of Intensive Care Unit Admission of Myasthenia Gravis: A Machine Learning-Based Approach.利用决策树算法支持重症肌无力患者入住重症监护病房的预测：一种基于机器学习的方法。

J Pers Med. 2022 Jan 2;12(1):32. doi: 10.3390/jpm12010032.

Exome and Whole Genome Sequencing in the Neonatal Intensive Care Unit.新生儿重症监护病房中的外显子组和全基因组测序。

Clin Perinatol. 2022 Mar;49(1):167-179. doi: 10.1016/j.clp.2021.11.018. Epub 2022 Jan 21.

The Role of Genome Sequencing in Neonatal Intensive Care Units.基因组测序在新生儿重症监护病房中的作用。

Annu Rev Genomics Hum Genet. 2022 Aug 31;23:427-448. doi: 10.1146/annurev-genom-120921-103442. Epub 2022 Jun 8.

Whole Genome Sequencing in Real-Time Investigation and Management of a Pseudomonas aeruginosa Outbreak on a Neonatal Intensive Care Unit.全基因组测序在新生儿重症监护病房铜绿假单胞菌暴发的实时调查与管理中的应用

Infect Control Hosp Epidemiol. 2015 Sep;36(9):1058-64. doi: 10.1017/ice.2015.133. Epub 2015 Jun 8.

A machine learning-based risk stratification tool for in-hospital mortality of intensive care unit patients with heart failure.基于机器学习的 ICU 心力衰竭患者院内死亡率风险分层工具。

J Transl Med. 2022 Mar 18;20(1):136. doi: 10.1186/s12967-022-03340-8.

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The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review.人工智能在新生儿重症监护病房的过去、现状与未来：一项系统综述

NPJ Digit Med. 2023 Nov 27;6(1):220. doi: 10.1038/s41746-023-00941-5.

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.

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.

Automated prioritization of sick newborns for whole genome sequencing using clinical natural language processing and machine learning.利用临床自然语言处理和机器学习技术对患病新生儿进行全基因组测序的自动优先级排序。

Genome Med. 2023 Mar 16;15(1):18. doi: 10.1186/s13073-023-01166-7.

PheNominal: an EHR-integrated web application for structured deep phenotyping at the point of care.PheNominal：一个在医疗护理点进行结构化深度表型分析的 EHR 集成型 Web 应用程序。

BMC Med Inform Decis Mak. 2022 Jul 28;22(Suppl 2):198. doi: 10.1186/s12911-022-01927-1.

Healthcare Professionals' Attitudes toward Rapid Whole Genome Sequencing in Pediatric Acute Care.医疗保健专业人员对儿科急性护理中快速全基因组测序的态度。

Children (Basel). 2022 Mar 4;9(3):357. doi: 10.3390/children9030357.

Effect of Whole-Genome Sequencing on the Clinical Management of Acutely Ill Infants With Suspected Genetic Disease: A Randomized Clinical Trial.全基因组测序对疑似遗传疾病急性病患儿临床管理的影响：一项随机临床试验。

JAMA Pediatr. 2021 Dec 1;175(12):1218-1226. doi: 10.1001/jamapediatrics.2021.3496.

Rapid whole-genome sequencing in critically Ill children: shifting from unease to evidence, education, and equitable implementation.危重症儿童的快速全基因组测序：从不安转向证据、教育及公平实施。

J Pediatr. 2021 Nov;238:343. doi: 10.1016/j.jpeds.2021.08.006. Epub 2021 Aug 11.

Project Baby Bear: Rapid precision care incorporating rWGS in 5 California children's hospitals demonstrates improved clinical outcomes and reduced costs of care.“熊宝宝计划”：在加利福尼亚的 5 家儿童医院中采用 rWGS（靶向全基因组测序）进行快速精准医疗，改善了临床结果并降低了医疗成本。

Am J Hum Genet. 2021 Jul 1;108(7):1231-1238. doi: 10.1016/j.ajhg.2021.05.008. Epub 2021 Jun 4.

Phenotypic signatures in clinical data enable systematic identification of patients for genetic testing.临床数据中的表型特征可用于系统地识别需要进行基因检测的患者。

Nat Med. 2021 Jun;27(6):1097-1104. doi: 10.1038/s41591-021-01356-z. Epub 2021 Jun 3.

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A Machine Learning Decision Support Tool Optimizes Whole Genome Sequencing Utilization in a Neonatal Intensive Care Unit.

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