电子健康记录中的机器学习:识别高风险产科患者在分娩前和分娩期间
Michal Lipschuetz1,2, Joshua Guedalia1, Sarah M Cohen1
1Obstetrics & Gynecology division, Hadassah-Hebrew University Medical Center, Jerusalem, Israel.
Studies in health technology and informatics
|July 25, 2024
概括
人工智能 (AI) 和机器学习 (ML) 使用电子健康记录预测分娩结果. 这些模型增强了个性化的出生管理,并减少了母亲和新生儿的并发症.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 在产科的统计分析.
背景情况:
- 怀孕和分娩涉及影响母亲和新生儿结果的复杂因素.
- 个性化的出生管理策略对于减少并发症至关重要.
- 电子健康记录 (EHR) 为临床研究提供了丰富的数据来源.
研究的目的:
- 将AI和统计分析应用于EHR数据,以了解怀孕和分娩的结果.
- 开发用于个性化出生管理和改善患者护理的预测模型.
- 通过数据驱动的洞察力,减少母亲和新生儿的并发症.
主要方法:
- 利用一个结构化的EHR数据库从大约13万个出生.
- 应用机器学习 (ML) 方法用于训练,测试和验证预测模型.
- 分析了一大数据集 (大约1.8亿个数据点) 用于模型开发.
主要成果:
- 在一般人群中,ML模型准确地预测了成功的阴道分娩,以及在剖腹产后试图进行分娩的妇女中.
- 一个实时动态模型表明,随着交付的进展,准确性越来越高.
- 开发了一个AI模型来预测意外剖腹产,突出了设施间数据变化的挑战.
结论:
- 人工智能和机器学习可以有效地使用EHR数据预测产科结果.
- 预测建模为个性化出生管理和提高患者安全提供了潜力.
- 这些技术可以帮助实现全球母亲和婴儿的安全分娩.
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