精神分裂症严重程度的多模式预测建模:利用随机森林和SVM的肝功能指标和认知得分
Sayed Sayem1, Sayed Sumsul Islam Sanny2, Rupali Hossain3
1Department of Statistics, Comilla University, Cumilla 3506, Bangladesh.
Psychiatry research. Neuroimaging
|July 27, 2025
概括
这项研究将肝功能测试 (阿兰胺转移酶,阿斯巴胺转移酶) 与使用机器学习预测精神分裂症严重程度的认知得分相结合,为临床评估显示出有希望的结果.
科学领域:
- 神经科学是一个神经科学.
- 肝病学 肝病学是一种肝病学.
- 计算精神病学是一种计算精神病学.
背景情况:
- 精神分裂症表现为认知缺陷和系统性障碍,通过肠-肝-脑轴与肝功能障碍的新兴联系.
- 将肝功能生物标志物整合到精神分裂症严重程度预测模型中尚未得到充分研究.
研究的目的:
- 开发和评估用于精神分裂症严重程度预测的多式机器学习框架.
- 将肝脏指标 (ALT,AST,胆红素,白蛋白,INR) 与认知评分相结合.
主要方法:
- 创建和预处理500个患者个人资料的合成数据集 (缺失归算,Min-Max正常化).
- 使用了两个机器学习模型:用于回归的随机森林和用于分类的带有 ECOC/RBF 内核的支持矢量机 (SVM).
- 进行特征重要性分析以确定关键预测因素.
主要成果:
- 随机森林回归器实现了0.70的R2,捕获了72%的差异.
- SVM分类器达到86.4%的准确性,AUC为0.91.
- 认知得分,氨酸氨基转移酶 (ALT) 和酸氨基转移酶 (AST) 被确定为主要预测因子.
结论:
- 综合性方法证明了精神分裂症严重程度评估的技术可行性和临床相关性.
- 除了认知评分外,利用肝脏生物标志物提供了强大的数据驱动的精神病评估方法.
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