机器学习方法用于怀孕和分娩风险管理
Georgy Kopanitsa1,2, Oleg Metsker2, Sergey Kovalchuk1
1Faculty of Digital Transformations, ITMO University, 4 Birzhevaya Liniya, 199034 Saint-Petersburg, Russia.
Journal of personalized medicine
|June 28, 2023
概括
机器学习模型可以使用电子健康记录来预测怀孕和分娩风险. 这项技术有助于管理产周护理,并改善母亲和婴儿的结果.
科学领域:
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
- 医疗保健中的机器学习
背景情况:
- 机器学习 (ML) 在医疗保健中提供数据驱动的决策支持,减少对明确规则设计的依赖.
- 早期识别和管理怀孕和分娩风险对于预防不良周产期结果至关重要.
- 临床决策支持系统 (CDSS) 可以减轻医疗专业的负担,但需要高质量,可解释的模型.
研究的目的:
- 调查ML方法用于预测分娩风险和预产日期的应用.
- 开发和验证可解释的ML模型,使用现实世界的围产期数据.
- 评估ML驱动的CDSS在增强产周护理方面的潜力.
主要方法:
- 在阿尔马佐夫专门医疗中心对12989名女性患者的电子健康记录进行了回顾性分析.
- 使用了包含73,115条记录的结构化和半结构化数据.
- 开发和评估了以性能和临床可解释性为重点的预测模型.
主要成果:
- 在设计用于分娩风险和预产日期预测的模型中实现了高预测性能.
- 证明了开发的ML模型的临床可解释性.
- 验证了ML在支持围产期护理决策方面的有效性.
结论:
- 机器学习方法为围产期护理中数据驱动的决策支持提供了强大的工具.
- 准确和可解释的ML模型可以显著提高个人患者管理和卫生系统组织.
- 这种方法为改善怀孕和分娩的风险管理,缓解和预防提供了机会.
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