基于机器学习对临床前健康问卷的评估
Calin Avram1, Adrian Gligor1, Dumitru Roman2
1George Emil Palade University of Medicine, Pharmacy, Science and Technology of Targu Mures, Romania.
International journal of medical informatics
|October 22, 2023
概括
机器学习 (ML) 模型可以使用患者问卷数据准确评估临床前的健康风险. 这项研究证明了98%准确的ML模型用于识别孕妇的健康状况,展示了高效的数据分析.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习应用 机器学习应用
- 公共卫生 公共卫生
背景情况:
- 从可穿戴设备和医疗设备等多种来源增加患者健康数据的可用性.
- 需要先进的分析技术来解释复杂的健康数据集.
- 专注于使用患者报告的信息进行临床前健康评估.
研究的目的:
- 研究现代机器学习 (ML) 技术在临床前健康评估中的应用.
- 通过使用问卷数据开发和评估一种ML模型,用于风险评估中的模式检测.
- 通过数据分析评估孕妇的健康状况.
主要方法:
- 在罗马尼亚穆雷什县分发给孕妇的问卷.
- 从已填写的问卷中提取和分析数据.
- 实现用于模式检测和风险评估的机器学习 (ML) 模型.
主要成果:
- 收集了1278名妇女的数据,特别关注怀孕期间的吸烟习惯 (381名吸烟者,216名戒烟者).
- 开发的ML模型在案例研究中实现了98%的高精度.
- 在临床环境中使用ML来分析问卷数据的可行性.
结论:
- 拟议的解决方案使问卷数据的有效数字化和分析成为可能.
- 机器学习方法需要减少计算资源 (内存和处理能力).
- 提供一种简单有效的方法,用于利用患者数据进行健康评估.
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