在医疗保健工作者中使用机器学习对心理痛苦的查:概念验证
Steve Geoffrion1,2, Catherine Morse3,4, Marie-Michèle Dufour4
1Research center of l'Institut universitaire en santé mentale de Montréal (CR-IUSMM), Montréal, Québec, Canada. s.geoffrion@umontreal.ca.
Journal of medical systems
|November 16, 2023
概括
机器学习模型可以使用两个问题和生物性别来识别有焦虑,抑郁和PTSD风险的医疗保健工作者. 需要进一步的数据来提高心理健康查模型的准确性.
科学领域:
- 精神病学和心理健康 精神病学和心理健康
- 计算医学是一种计算医学.
- 流行病学 流行病学
背景情况:
- 在COVID-19大流行期间,医疗保健工作者面临着严重的心理痛苦.
- 早期识别心理健康状况,如焦虑,抑郁和PTSD对于及时干预至关重要.
- 现有的选方法对于频繁的监测可能是繁重的.
研究的目的:
- 开发和验证初步的机器学习模型,以识别有焦虑,抑郁和PTSD风险的医疗保健工作者.
- 评估使用最小数据 (两个问题和生物性别) 进行心理困境查的可行性.
- 探索通过数据驱动模型减少评估负担的潜力.
主要方法:
- 在COVID-19的前两波期间,对816名医疗保健工作者进行了前性队列研究.
- 通过移动应用程序每周收集数据,包括11个问题和3个验证的查问卷.
- 使用五重交叉验证开发和验证后勤回归和支向量机器模型.
- 功能选择侧重于两个关键问题和生物性别.
主要成果:
- 机器学习模型在识别焦虑,抑郁和创伤后应激障碍的积极查得分方面达到70%至80%的准确性.
- 模型的积极预测值不超过50%,表明需要更多数据.
- 该研究展示了使用机器学习的概念验证,使用有限的数据进行选.
结论:
- 机器学习提供了一种可行的方法,用于开发新的查模型,用于医疗保健工作者的心理痛苦.
- 使用最小数据的模型显示出减轻每周评估负担的前景.
- 需要对更大的数据集进行进一步的研究,以提高这些模型的预测准确性和临床实用性.
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