预测社区精神卫生服务中的焦虑治疗结果,使用链接的卫生行政数据
Kevin E K Chai1, Kyran Graham-Schmidt2, Crystal M Y Lee3
1School of Population Health, Curtin University, Perth, WA, Australia. k.chai@curtin.edu.au.
Scientific reports
|September 4, 2024
概括
使用机器学习预测焦虑症治疗结果显示中等成功. 治疗前的痛苦分数和医疗服务数据可以预测改善,强调需要持续监测患者和更丰富的数据收集,以获得更好的个性化护理.
科学领域:
- 心理健康研究 心理健康研究
- 计算精神病学是一种计算精神病学.
- 医疗保健服务研究 医疗服务研究
背景情况:
- 焦虑障碍是全球领先的精神疾病,影响着数以百万计的人,并对日常生活产生重大影响.
- 个性化护理和优化资源配置对于改善患者在心理健康治疗中的结果至关重要.
研究的目的:
- 为了确定社区心理健康治疗焦虑障碍是否会导致可靠的变化,Kessler心理困扰量表 (K10) 得分.
- 评估治疗前K10分数和过去的医疗服务相互作用的预测准确性,以获得可靠的治疗改变 (改善).
主要方法:
- 利用了西澳大利亚46,938名公共患者 (2005-2022) 患有焦虑相关疾病的大数据集.
- 开发了机器学习模型 (分类和回归),使用链接的卫生服务行政数据,包括K10分数和服务利用率.
- 计算了K10分数的可靠变化,并将其用作模型训练和测试的结果变量.
主要成果:
- 机器学习模型实现了适度的预测性能:分类模型的接收器运行特征曲线 (AUC) 下的面积为0.76和F1得分为0.69.
- 回归模型实现了0.37的R平方和5.58.8的平均绝对误差 (MAE).
- 这些结果表明了预测的潜力,但突出了当前数据的局限性.
结论:
- 社区心理健康治疗与焦虑障碍的K10分数的可靠变化有关.
- 虽然预测模型表现中等,但它们强调需要定期监测患者.
- 收集更多临床相关和上下文相关的患者数据对于提高治疗结果的预测和个性化护理至关重要.
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