通过机器学习预测诊断进展到精神分裂症或双极性障碍.
Lasse Hansen1,2,3, Martin Bernstorff1,2,3, Kenneth Enevoldsen1,3
1Department of Affective Disorders, Aarhus University Hospital-Psychiatry, Aarhus, Denmark.
JAMA psychiatry
|February 19, 2025
概括
机器学习模型可以使用电子健康记录来预测精神分裂症和双相情感障碍过渡. 精神分裂症的预测比双相情感障碍更准确,突出了早期干预的潜力.
科学领域:
- 精神病学是一个精神病学.
- 机器学习 机器学习
- 医疗信息学 医疗信息学
背景情况:
- 精神分裂症和双相情感障碍的诊断往往会延迟,阻碍及时治疗.
- 早期干预至关重要,因为这些情况通常在青春晚期或成年早期出现.
研究的目的:
- 评估机器学习模型,以预测诊断进展到精神分裂症或双相情感障碍.
- 评估常规电子健康记录 (EHR) 数据对早期检测的有用性.
主要方法:
- 一项队列研究利用了来自丹麦中部地区精神病医疗服务的EHR数据.
- 机器学习模型 (逻辑回归,XGBoost) 在临床数据上接受训练,包括诊断,药物和笔记.
- 模型性能使用接收器操作特征曲线 (AUROC) 下的面积来评估.
主要成果:
- 在预测过渡到精神分裂症或双相情感障碍的测试组中,XGBoost模型获得了0.64的AUROC.
- 精神分裂症的预测准确度更高 (AUROC,0.80) 比双相情感障碍 (AUROC,0.62).
- 临床笔记被确定为特别有价值的预测指标.
结论:
- 机器学习模型可以使用常规EHR数据预测诊断过渡到精神分裂症和双相情感障碍.
- 早期预测是可行的,可能使更早的治疗启动.
- 与双相情感障碍预测相比,精神分裂症预测的有效性更高.
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