怀孕高血压疾病中的AI:审查
Ruben D Zapata1, Tioluwani Tolani2, Rebecca Reich3
1University of Florida, Department of Health Outcomes and Biomedical Informatics, College of Medicine, Gainesville, FL, USA.
American journal of hypertension
|April 9, 2025
概括
机器学习 (ML) 在改善妊娠高血压障碍 (HDP) 检测和风险预测方面表现有前途. 然而,数据的差异和对干预时间的有限关注凸显了未来在HDP的ML研究领域.
科学领域:
- 产科和妇科 产科和妇科
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
背景情况:
- 妊娠高血压疾病 (HDP) 是全球孕产妇和胎儿死亡的主要原因.
- 早期检测和风险分层对于预防严重的HDP并发症至关重要.
- 传统的临床方法往往缺乏有效风险识别的精度.
研究的目的:
- 系统地审查机器学习 (ML) 在妊娠高血压障碍 (HDP) 中的应用.
- 为了确定趋势,方法和研究差距在ML为HDP.
- 引导未来的研究,以改善HDP的孕产妇和胎儿结果.
主要方法:
- 根据PRISMA-ScR指南进行范围审查.
- 在HDP中对ML模型的英语出版物的三个数据库进行了全面的搜索.
- 使用CHARMS检查清单提取数据,总结研究特征,结果和ML方法.
主要成果:
- 大多数研究都集中在产前 (75.27%),其他HDP表型的代表性有限.
- 自2014年以来,用于诊断/发病检测 (62.37%) 和风险预测 (27.95%) 的机器学习应用程序增加了.
- 在中国和北美地区的地理集中;omics和成像数据的不足利用;对干预时间和再接收结果的报告有限.
结论:
- 机器学习显示了HDP诊断和风险预测的显著增长.
- 地理差异,表型代表性有限,干预时间模型的代表性不足是关键障碍.
- 未来的研究应侧重于各种数据集和模型,以改善HDP的干预时间和风险概况.
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