开发和验证用于预测精神疾病患者2型糖尿病的机器学习模型
Martin Bernstorff1,2,3, Lasse Hansen1,2,3, Kenneth Enevoldsen1,2,3
1Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark.
Acta psychiatrica Scandinavica
|April 4, 2024
概括
机器学习使用电子健康记录准确地预测精神疾病患者的2型糖尿病 (T2D). 在这个人群中早期识别T2D风险可以为预防策略提供信息.
科学领域:
- 临床信息学 临床信息学
- 机器学习应用 机器学习应用
- 公共卫生 公共卫生
背景情况:
- 2型糖尿病 (T2D) 在患有精神疾病的人群中患病率是其两倍.
- 在这个人口群体中,早期干预是预防T2D的关键.
- 准确的风险预测对于及时干预至关重要.
研究的目的:
- 开发和验证用于T2D预测的机器学习模型.
- 为了识别患有精神疾病且患T2D风险较高的患者.
- 为了利用常规临床数据进行预测建模.
主要方法:
- 利用来自丹麦精神病医疗服务的电子健康记录 (74880名患者).
- 从51个变量 (人口统计,诊断,治疗,实验室) 中提取了1343个预测因素.
- 在85%的数据上训练了XGBoost和后勤回归模型,在15%的数据上进行了验证.
主要成果:
- XGBoost模型预测T2D风险可以提前2.7年.
- 获得了0.84.84的接收器运行特征曲线 (AUC) 下的面积.
- 在测试组中确定了31%的T2D病例,至少有一个积极的预测.
结论:
- 机器学习使用EHR数据准确地预测精神疾病患者的T2D.
- 一个决策支持系统可以指导这种高风险群体的T2D预防.
- 这种方法有助于为弱势群体提供积极的医疗保健.
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