利用机器学习来检查与数字治疗方法的参与
Andrew C Heusser1, Denton J DeLoss1, Elena Cañadas1
1Akili Interactive, Boston, MA, United States.
Frontiers in digital health
|June 19, 2023
概括
我们开发了一种机器学习方法来评估患者如何使用数字疗法 (DTx). 该技术准确地实时测量参与质量,改善治疗监测和个性化,以获得更好的健康结果.
科学领域:
- 数字健康干预措施 数字健康干预措施
- 机器学习在医疗保健中的应用
- 人与计算机的互动.
背景情况:
- 数字治疗 (DTx) 是针对医疗条件的基于软件的干预措施.
- DTx生成客观数据患者参与,包括交互质量.
- 对认知干预等治疗方法来说,评估参与的质量至关重要.
研究的目的:
- 介绍一种用于实时测量用户与数字处理交互质量的新技术.
- 评估该技术在对用户参与度进行分类方面的有效性.
- 探索临床决策和研究中的应用.
主要方法:
- 开发了一种机器学习模型,在专家标记的数据上进行训练.
- 在多任务认知训练任务 (任务) 期间的分类用户交互.
- 在一个持久的测试集上评估了模型的性能.
主要成果:
- 机器学习分类器在预测专家衍生的标签方面获得了高准确度 (0.94) 和F1得分 (0.94).
- 该技术可靠地区分"按预期使用"和"不按预期使用"的用户.
- 该方法提供了在任务中的评估 (大约. 4分钟) 的水平.
结论:
- 这种技术为评估患者与DTx.x.接触的质量提供了有价值的工具.
- 研究结果支持患者,护理人员和医疗保健提供者之间加强共享决策和沟通.
- 该方法在临床试验和个性化数字干预中具有潜在的应用.
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