识别为月经前症状寻求正式帮助的预测因素:对症状,功能障碍和障碍数据的机器学习分析
Erin L Funnell1, Nayra A Martin-Key1, Jakub Tomasik1
1Cambridge Centre for Neuropsychiatric Research (CCNR), Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom.
很少有女性为严重的月经前综合征 (PMS) 寻求正式护理. 社会功能受损,症状严重程度的感知和过去不良的护理经验预测了寻求帮助的PMS,突出需要更好的意识和指导.
科学领域:
- 生殖健康 生殖健康
- 心理健康 心理健康
- 医疗保健服务研究 医疗服务研究
背景情况:
- 月经前症状 (PMS) 可能是严重和繁重的,但正式的医疗保健寻求仍然很低.
- 了解正式寻求帮助的预测因素对于获得治疗干预至关重要.
- 很少有研究利用机器学习来确定影响寻求PMS帮助的因素.
研究的目的:
- 通过机器学习识别症状,功能障碍和进入障碍,预测PMS的正式寻求帮助.
- 分析经历月经前症状的个体中正式寻求帮助行为的预测因素.
主要方法:
- 一项总部位于英国的在线调查收集了592名患有月经前症状的参与者的数据.
- 描述性分析,曼·惠特尼U测试和奇平方测试检查了群体差异.
- 极端梯度增强 (XGBoost) 机器学习模型预测了正式的寻求帮助行为.
主要成果:
- 该XGBoost模型显示了公平的预测性能 (AUROC=0.75).
- 关键预测因素包括社会功能障碍,感知症状严重程度,工作/学习障碍和以前的妇科护理经验不佳.
- 超过57%的参与者以前曾为PMS寻求过正式的护理.
结论:
- 功能障碍和过去的负面照顾经历是PMS正式寻求帮助的重要预测因素.
- 要提高正式护理率,需要对PMS的更深入的了解和寻求帮助的明确指导.
- 需要进一步的研究,以了解糟糕的护理经历如何影响寻求PMS帮助.
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