机器学习和人工智能如何在数字行为改变干预中使用? 一个范围广泛的审查
Amy Bucher1, E Susanne Blazek1, Christopher T Symons1
1Behavioral Reinforcement Learning Lab (BReLL), Lirio, Knoxville, TN.
Mayo Clinic proceedings. Digital health
|April 10, 2025
概括
机器学习 (ML) 和人工智能 (AI) 在数字行为改变干预 (DBCI) 中显示出对现实世界健康的承诺. 需要进一步的研究来规范人工智能术语,并了解长期影响.
科学领域:
- 数字健康数字健康
- 行为科学 行为科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 数字行为改变干预 (DBCI) 越来越多地结合机器学习 (ML) 和人工智能 (AI).
- 评估ML / AI在DBCI影响患者和消费者健康行为的现实应用至关重要.
- 了解使用的ML / AI类型及其在行为改变中的有效性是一个活跃的研究领域.
研究的目的:
- 进行现场DBCI的范围审查,利用ML或AI来影响现实世界的健康行为.
- 识别行为领域,目标行为和特定的ML/AI功能.
- 评估与这些ML/AI驱动的DBCI相关的研究质量和局限性.
主要方法:
- 在EMBASE,PsycInfo,PsycNet,PubMed和Web of Science数据库中进行了全面的范围审查.
- 搜索术语包括ML / AI,行为科学和数字健康,专注于现场DBCI.
- 32篇文章符合纳入标准,其中提取的数据包括行为领域,目标行为,ML / AI类型和目的以及研究评估.
主要成果:
- 发现23个DBCI使用人工智能来影响现实世界的健康行为.
- 心脏代谢健康 (21.7%) 和生活方式干预 (17.4%) 是最常见的领域.
- 经典的ML算法 (43.5%),强化学习 (34.8%) 和自然语言理解 (34.8%) 是普遍存在的ML/AI类型.
结论:
- 在DBCI中的AI显示了管理复杂数据和为行为改变提供个性化的支持的前景.
- 证据表明积极的结果,但突出局限性,包括缺乏因果检测,低概括性和不足的长期数据.
- 标准化术语和提高对DBCI中的ML/AI应用的理解是未来研究和开发的关键机会.
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