使用主题建模促进在线医疗保健支持小组的形成.
Pronob Kumar Barman1, Tera L Reynolds1, James Foulds1
1Department of Information Systems, University of Maryland, Baltimore County.
Studies in health technology and informatics
|August 8, 2025
概括
本研究引入了特定于组的迪里克莱特多项式回归 (gDMR) 模型,以创建个性化的在线患者支持小组. gDMR通过自动形成更相关和更连贯的群体来增强患者的参与和护理.
科学领域:
- 医疗信息学 医疗信息学
- 计算社会科学 计算社会科学
- 数字健康数字健康
背景情况:
- 在线健康论坛很受同行支持的欢迎,但往往缺乏个性化和一致的参与.
- 由于互动模式不一致,大型论坛难以满足患者的各种需求.
- 规模较小,量身定制的支持小组为个性化患者互动提供了潜在的解决方案.
研究的目的:
- 引入一个新的框架,集团特定的迪里克莱特多项式回归 (gDMR),用于自动化和个性化支持组的形成.
- 通过利用用户生成的内容,人口统计和交互数据来增强患者支持.
- 提高患者支持小组的语义连贯性和上下文相关性.
主要方法:
- 开发了特定于集团的迪里克莱特多项式回归 (gDMR) 模型,扩展了传统的迪里克莱特多项式回归 (DMR).
- 整合了特定组的参数和节点嵌入,以捕捉微妙的用户模式.
- 利用用户生成的内容,人口统计信息和交互数据进行组建.
主要成果:
- 与基线模型相比,gDMR模型成功形成了比基线模型更有语义连贯性和上下文相关性的组.
- 展示了gDMR模型的可扩展性,用于支持组的自动化个性化.
- 展示了减少在定制支持小组的手工努力,以提高患者参与度.
结论:
- 在数字医疗保健中,gDMR模型为个性化患者支持小组提供了一个可扩展和有效的框架.
- 这种方法可以促进更具包容性的参与,并改善以患者为中心的护理.
- 结果表明gDMR在数字健康平台上的适用性,包括在线健康社区和电子健康记录 (EHR).
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