机器学习,先进的数据分析,以及在怀孕护理中的作用? 我们如何帮助改善孕前的结果?
Annemarie Hennessy1, Tu Hao Tran2, Suraj Narayanan Sasikumar3
1Campbelltown Hospital, South Western Sydney Local Health District, Sydney, Australia; Western Sydney University, Sydney, Australia; University of Sydney, Sydney, Australia.
Pregnancy hypertension
|June 14, 2024
概括
高质量的数据和多样化的群体对于在孕产妇健康方面实现机器学习 (ML) 至关重要,尤其是在预测产前方面. 临床医生应该了解ML和AI,以改善对母亲和家庭的护理.
科学领域:
- 孕产妇健康 孕产妇健康
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 机器学习 (ML) 具有显著的潜力,可以改善母亲的健康状况,特别是在预测产前方面.
- 实现ML的全部价值需要高质量,具有代表性的临床数据和多样化的医疗保健环境.
研究的目的:
- 提供机器学习术语和早期结果在怀孕和产前的背景下概述.
- 鼓励临床医生了解ML和人工智能 (AI) 在改善母亲和家庭健康结果方面的潜力和实用性.
主要方法:
- 本综述综合了关于ML在孕产妇健康和孕前预测中的应用现有的文献.
- 它概述了与临床医生相关的ML的关键定义和特征.
- 该审查探讨了未来的可能性,以及在人工智能驱动的医疗保健中定义风险和结果的重要性.
主要成果:
- 对于预孕前的预测,ML的有效实施取决于强大的数据质量和人口代表性.
- 早期的结果表明ML的潜力,但广泛采用需要解决数据和系统层面的挑战.
- 了解ML / AI对于临床医生利用这些技术至关重要.
结论:
- 临床医生需要拥抱ML和AI,以提高产前的预测和管理.
- 未来的进步取决于整合实时数据,比较不同的医疗保健模型,并培养快速应用的文化.
- 清楚地定义风险和结果对于人工智能在产科的成功应用至关重要.
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