超越步数计数:我们是否准备好使用数字表型来在精神病学中做出可行的个人预测?
Abigail Ortiz1, Benoit H Mulsant1
1Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
Journal of medical Internet research
|August 5, 2024
概括
目前的心理健康预测缺乏个人的准确性. 数字表型表现有希望,但需要一种结构化的方法,比如用于心血管疾病的方法,用于精神病学的临床应用.
科学领域:
- 精神病学是一个精神病学.
- 数字健康数字健康
- 计算神经科学是一种神经科学.
背景情况:
- 现有的心理障碍人口统计和临床模型缺乏个体预测准确度.
- 生物标志物还没有达到精密精神病学所需的灵敏度或特异性.
- 技术进步使持续的客观和密集的主观行为数据收集成为可能.
研究的目的:
- 为精神病学中数字表型设计提出一个逐步开发和实施的战略.
- 为了实现心理障碍的临床可行的预测.
- 为了弥合数据收集和临床实用性之间的差距.
主要方法:
- 审查目前精神病预测模型的局限性.
- 分析数字表型化的潜力和挑战.
- 建议采用结构化,逐步的方法,类似于心血管疾病预测.
主要成果:
- 数字表型化数据虽然丰富,但在最近的试验中尚未改善临床结果.
- 要将数字表型化数据转化为可操作的临床见解,需要采用结构化的方法.
- 群体之间生物标志物的显著差异并不能保证个体的诊断或预后能力.
结论:
- 一种系统的,逐步的方法对于将数字表型化整合到临床精神病学中至关重要.
- 从其他领域 (如心血管疾病) 的成功预测模型中学习是关键.
- 实现精确精神病学需要超越数据收集,进行验证,可操作的预测.
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