通过机器学习减少患有慢性疾病的人群的治疗负担:观点
Harpreet Nagra1, Aradhana Goel2, Dan Goldner1
1One Drop, New York, NY, United States.
JMIR biomedical engineering
|June 14, 2024
概括
数字健康 (电子健康) 可以减少慢性疾病的治疗负担. 新兴的机器学习模型提供了一种支持患者和预防倦怠的新方法.
科学领域:
- 医疗信息学 医疗信息学
- 慢性疾病管理 慢性疾病管理
- 数字健康数字健康
背景情况:
- COVID-19大流行突出了医疗保健系统的挑战,尤其是慢性病患者.
- 数字健康技术 (电子健康) 为改善护理质量和自我管理提供了潜在的解决方案.
- 现有的电子卫生模型缺乏全面的实施框架.
研究的目的:
- 在慢性疾病中定义治疗负担和倦怠风险.
- 评估电子健康增强的慢性护理模型对优先考虑数字健康解决方案的实用性.
- 引入一种旨在减轻治疗负担和倦怠的机器学习模型.
主要方法:
- 文献审查和概念框架的开发.
- 治疗负担和倦怠的定义和分析.
- 描述一种新兴的慢性护理机器学习模型.
主要成果:
- 治疗负担和倦怠是慢性病管理中的重大挑战.
- 通过电子健康增强的慢性护理模型可以指导数字健康干预措施的选择.
- 机器学习模型在降低患者治疗负担方面表现有前途.
结论:
- 电子健康,特别是机器学习,为支持慢性疾病管理提供了颠覆性机会.
- 解决治疗负担和倦怠问题对于改善患者的治疗结果至关重要.
- 进一步开发和实施电子健康解决方案是有必要的.
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