对处方兴奋剂心血管风险较高的患者进行表征:通过预测分析和数据挖掘技术从健康记录数据中学习
Yifang Yan1, Qiushi Chen1, Rafay Nasir2
1The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA, USA.
在患有注意力缺陷/多动障碍 (ADHD) 的成年人中,处方兴奋剂的使用与心血管风险增加有关. 机器学习识别了患者的特征,包括并发症和物质使用,这些特征在处方兴奋剂时表明风险更高.
科学领域:
- 心脏病学 心脏病学
- 精神病学是一个精神病学.
- 数据科学数据科学数据科学
背景情况:
- 对于患有注意力缺陷/多动症障碍 (ADHD) 的成年人来说,增加了兴奋剂的处方,引发了人们对心血管事件风险的担忧.
- 量化这种额外的风险和确定风险患者的个人资料对于安全的处方至关重要.
研究的目的:
- 量化与成年人处方兴奋剂使用相关的心血管事件的额外风险.
- 用机器学习和数据挖掘来描述受兴奋剂治疗不利影响的患者.
主要方法:
- 利用TrinetX (2010-2020) 的成年ADHD患者的电子健康记录.
- 开发和比较机器学习模型来预测一年内心血管风险.
- 雇员协会规则挖掘 (ARM) 以确定具有不良结果的患者的临床特征.
主要成果:
- 该研究包括219,965名成年人,其中102,138人接受兴奋剂治疗.
- 机器学习模型在预测心血管风险方面取得了很高的准确性 (AUC 0.77-0.84).
- ARM确定了关键的风险因素,包括并发症,先前的心血管事件和物质/心理障碍.
结论:
- 集成的预测建模和数据挖掘,以描述使用兴奋剂更高风险的患者.
- 每个年龄组的合并疾病的验证观察清单突出显示了增加的风险.
- 需要对已识别的特征进行外部验证,以指导更安全的兴奋剂处方实践.
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