生物标记符号作为精神分裂症未来冲动性的预测:一个多中心研究
Siqi Liu1, Yixiao Chen2, Lei Zhang1
1Shanghai Putuo District Mental Health Center, Shanghai, China.
Frontiers in psychiatry
|October 15, 2025
概括
常规生物标志物可以预测精神分裂症患者未来的冲动性. 整合这些生物标志物和临床数据的机器学习模型显示出有希望的预后价值,观察到性别特异性模式.
科学领域:
- 精神病学是一个精神病学.
- 生物标志物 生物标志物
- 机器学习 机器学习
背景情况:
- 临床冲动性尺度很常见,但实验室生物标志物证据有限.
- 精神分裂症 (SCZ) 患者经常表现出冲动性,影响治疗结果.
研究的目的:
- 评估SCZ中常规收集的冲动性风险生物标志物的预后价值.
- 开发和验证用于预测SCZ患者冲动性的机器学习模型.
主要方法:
- 分析了1496名首次入院的SCZ患者的数据.
- 利用了99个特征,包括91个常规生物标志物,并使用冲动行为风险评估尺度评估冲动性.
- 训练并验证了五种机器学习模型,使用AUROC评估性能,使用SHAP评估生物标志物的重要性.
主要成果:
- CatBoost模型实现了0.749的AUROC (交叉验证) 和0.719 (外部测试).
- 关键的生物标志物包括尿酸,血球蛋白和阿波利波蛋白E,具有异质的反应模式.
- 将生物标志物与临床数据相结合,提高了预测准确度.
结论:
- 常规生物标志物对于早期识别SCZ中高冲动性风险是有价值的.
- 性别特定因素是SCZ冲动性预测建模的重要考虑因素.
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