NeoGx:新生儿机器推的快速基因组测序
Austin A Antoniou1,2, Regan McGinley3, Marina Metzler4,5
1The Office of Data Sciences, The Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, OH, USA.
medRxiv : the preprint server for health sciences
|July 9, 2024
概括
机器学习算法可以预测新生儿重症监护室 (NICU) 患者需要进行基因检测的需求. 这加快了基因评估,减少了诊断旅程,改善了患者的治疗结果.
科学领域:
- 医疗信息学 医疗信息学
- 遗传学 遗传学 是一个
- 新生儿科学 新生儿科学
背景情况:
- 在第四级新生儿重症监护室 (NICU) 中,遗传疾病很普遍.
- 新生儿科提供者可能并不总是认识到新生儿基因评估的必要性.
- 健康记录的表型可以用来训练机器学习模型来预测遗传测试需求.
研究的目的:
- 开发和验证机器学习 (ML) 算法,以预测新生儿基因测试的必要性.
- 评估ML驱动的基因测试预测对诊断旅程长度和解析时间的影响.
主要方法:
- 从使用自然语言处理 (NLP) 的临床文本中提取了人体现象本体学 (HPO) 术语,用于NICU患者的十年.
- 训练并选择了考虑各种特征集,架构和超参数的分类器.
- 在2,241名IV级NICU入院患者 (2020-2021年出生) 的队列上验证了ML分类器.
主要成果:
- 在新生病重症监护室入院后的第一周内,ML分类器实现了0.87的ROC AUC和0.73的PR AUC.
- 使用ML加速初始遗传测试,将初始遗传测试的中位时间从10天减少到1天.
- 与快速遗传测序 (rGS) 相结合的ML预测,与基线相比,在14天内增加了3.8倍的诊断旅程的解决方案.
结论:
- 机器学习预测可以显著加速新生儿的遗传评估.
- 实施ML工具有助于提供者识别基因测试的需要.
- 较早和有针对性的基因测试可以改善新生儿重症监护室患者的治疗结果.
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