一个基于机器学习的临床决策支持系统,用于有效分层妊娠糖尿病和通过阿育吠陀进行管理
Nisha P Shetty1, Jayashree Shetty1, Veeraj Hegde2
1Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
Journal of Ayurveda and integrative medicine
|December 11, 2024
概括
机器学习可以准确地预测怀孕期糖尿病 (GDM) 孕妇的风险因素. 通过人工智能的早期检测有助于及时进行阿育吠陀干预,改善孕产妇的健康结果.
科学领域:
- 计算医学是一种计算医学.
- 生殖健康 生殖健康
- 综合医学是一个整体的医学.
背景情况:
- 孕期糖尿病 (GDM) 是一种与怀孕相关的代谢状况,其特征是高血糖.
- 早期预测GDM对于有效的管理和改善母亲的结果至关重要.
- 机器学习的进步为GDM风险评估提供了新的方法.
研究的目的:
- 应用机器学习算法来预测妊娠糖尿病的危险因素.
- 评估各种机器学习模型在GDM预测中的性能.
- 探索阿尤尔韦达干预措施用于GDM管理.
主要方法:
- 利用机器学习算法来分析GDM风险因素.
- 使用准确度,精度和F1分数等指标评估算法性能.
- 对生活方式干预和草药疗法的阿育韦德文献进行了审查.
主要成果:
- 机器学习分类器的预测准确度在75-82%之间.
- 改变生活方式,草药和阿育吠陀疗法在降低GDM风险方面表现出有效性.
- 通过机器学习早期发现GDM可以提高阿育吠陀干预措施的有效性.
结论:
- 确定了影响孕妇GDM的关键因素.
- 强调了饮食,运动和阿育吠陀管理 (加尔比尼帕里查利亚) 控制GDM的重要性.
- 早期诊断与整体管理策略相结合,可以减轻GDM并发症.
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