评估和实施机器学习模型,以提供个性化的移动健康应用程序建议
Hafsat Morenigbade1, Tareq Al Jaber2, Neil Gordon2
1Centre of Excellence for Data Science, AI and Modelling, Faculty of Science and Engineering, University of Hull, United Kingdom.
PloS one
|March 19, 2025
概括
这项研究使用机器学习开发了一个医疗保健应用程序推系统. 伯特模型的准确度达到了90%左右,为用户改善了移动健康 (mHealth) 应用程序的发现.
科学领域:
- 数字健康数字健康
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 移动健康 (mHealth) 应用程序的普及需要有效的评估和推系统.
- 越来越多的mHealth市场强调了需要工具来引导用户到相关的健康应用程序.
研究的目的:
- 为移动健康应用设计和评估推系统.
- 为了利用应用程序属性,特别是描述,进行上下文用户建议.
- 为了改善用户体验,对健康应用进行分类.
主要方法:
- 一个健康应用程序属性的数据集,包括描述,评分和评论,被策划.
- 数据预处理涉及一次性编码,标准化和功能工程,包括一个新的"评分_评论"功能.
- 机器学习和深度学习模型,包括随机森林和BERT,使用"类别" (例如"减肥"",医疗") 作为目标变量进行评估.
主要成果:
- 采用转移学习的BERT模型表现出高效率,在超参数调整后达到约90%的准确性.
- 功能工程,包括"评分_评论"指标,有助于模型性能.
- "类别"变量有效地区分了mHealth景观中的不同健康背景.
结论:
- 转移学习,特别是与BERT模型,是非常有效的mHealth应用推.
- 使用应用程序描述和以用户为中心的功能,可以构建一个强大的推系统.
- 开发的系统采用了共弦相似性,根据用户查询相关性准确排名应用程序.
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