在医疗保健中使用自主监督机器学习验证人员的新型数据驱动方法
Emanuele Tauro1, Alessandra Gorini2, Grzegorz Bilo3
1Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy; Department of Cardiology, Cardiology Research Laboratory, Istituto Auxologico Italiano IRCCS, Milan, Italy.
这项研究引入了一种新的自主监督机器学习 (SSML) 方法,用于使用现有数据验证个人. 这种方法有效地对人口进行分层,并大大降低了验证成本.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 医疗保健分析 医疗保健分析
背景情况:
- 人格验证传统上依赖于昂贵的外部方法.
- 在人格创建过程中,现有的数据往往被不足以用于验证.
研究的目的:
- 开发一种新的,具有成本效益的个人验证方法.
- 为了利用现有数据进行人格验证.
- 使用自主监督机器学习进行人格验证.
主要方法:
- 开发了一种自主监督机器学习 (SSML) 方法.
- 数据被分为培训 (80%) 和测试 (20%) 组.
- 通过5倍的交叉验证确定了最佳模型,最终预测得到了多数投票.
主要成果:
- 在两个不同的医疗保健数据集上测试了SSML方法.
- 实现了高性能指标:加权精度 (高达94.12%),精度 (高达92.83%),回忆 (高达91.67%) 和F1得分 (高达91.76%).
- 该方法证明了人格集群的有效差异化.
结论:
- 拟议的SSML方法为个人验证提供了强大的概括能力.
- 它成功地验证了personas对目标人群分层的能力.
- 与现有方法相比,这种方法显著降低了人格验证的成本.
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