机器学习:心血管妊娠生理学和心脏产科研究的新时代
Contessa A Ricci1,2,3, Benjamin Crysup4,5, Nicole R Phillips4
1College of Nursing, Washington State University, Spokane, Washington, United States.
概括
本综述探讨了机器学习 (ML) 如何改善对孕期和产后母亲心血管适应的理解. ML提供工具来预测心血管并发症,增强产妇护理和降低死亡风险.
科学领域:
- 心血管生理学心血管生理学
- 生殖医学 生殖医学
- 医疗信息学 医疗信息学
背景情况:
- 怀孕引发了重要的心血管适应,以满足代谢需求.
- 孕产妇的心血管并发症是导致死亡的主要原因,预测工具有限.
- 现有的研究缺乏对健康怀孕心血管适应的全面了解.
研究的目的:
- 审查机器学习 (ML) 应用程序,以了解怀孕期间的心血管生理学.
- 探索基于ML的预测模型,用于怀孕患者的临床使用.
- 讨论 ML 平台,可解释性,偏见和这一领域的伦理考虑.
主要方法:
- 对利用机器学习用于怀孕期间心血管研究的研究进行文献综述.
- 对与怀孕有关的心血管数据应用的ML平台和算法的分析.
- 讨论在孕产妇心血管健康中对ML的挑战和未来方向.
主要成果:
- 机器学习为研究怀孕期间的复杂心血管适应提供了新的工具.
- 机器学习模型显示出开发心血管并发症预测工具的前景.
- 目前的研究突出了ML的潜力,但也确定了理解和应用的差距.
结论:
- 机器学习是一种强大的方法,可以揭开母亲的心血管适应和风险.
- 开发强大,可解释和道德的ML模型对于临床翻译至关重要.
- 需要进一步的研究来充分利用ML以改善母亲的心血管结果.
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