一个系统的回顾机器学习发现在PTSD和他们的关系与理论模型的理论模型
Wivine Blekic1, Fabien D'Hondt1,2, Arieh Y Shalev3
1Univ. Lille, Inserm, CHU Lille, U1172-LilNCog-Lille Neuroscience & Cognition, Lille, France.
Nature. Mental health
|February 17, 2025
概括
机器学习 (ML) 有效地识别创伤后应激障碍 (PTSD) 风险因素,与现有理论保持一致,并揭示新的见解. 本综述提出了一个综合性PTSD风险模型,强调在心理健康研究中标准化ML应用.
科学领域:
- 精神病学和心理健康 精神病学和心理健康
- 计算神经科学是一种神经科学.
- 在医疗保健中的数据科学.
背景情况:
- 机器学习 (ML) 越来越多地用于预测创伤后应激障碍 (PTSD).
- 临床相关性和ML发现的概括性仍然是实施的挑战.
- 需要进行系统审查,以评估ML对了解PTSD风险因素的贡献.
研究的目的:
- 根据理论理解,评估ML识别的PTSD风险因素.
- 识别PTSD研究中的ML技术的新见解.
- 使用ML和现有研究开发一个整合性PTSD风险模型.
主要方法:
- 在PubMed, Web of Science和Scopus中对PTSD风险因素的ML研究进行系统审查.
- 纳入标准:确定预测因素和PTSD症状与创伤的时间关系的研究.
- 提取并对15个关键预测因素进行分类;使用PROBAST工具评估偏差.
主要成果:
- 包括30项研究 (12,908名参与者),揭示了预测因素的重叠和与理论的一致性.
- ML确定了未被充分研究的PTSD风险因素.
- 许多研究表明存在偏见的风险,突出了方法标准的必要性.
结论:
- 在识别PTSD风险因素方面,ML方法显示出有前途,补充理论模型.
- 提出了一种综合性PTSD风险模型,包括基于数据和理论的发现.
- 在心理健康研究中,对ML的标准化应用和报告至关重要.
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