一个基于机器学习算法的精神分裂症临床预测模型.
Weifeng Jin1, Shuzi Chen1, Qiong Gao1
1Department of Medical Laboratory, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
这项研究使用机器学习和常规血液检查开发了一种用于精神分裂症的辅助诊断工具. 后勤回归模型显示出作为早期精神分裂症检测的诊断辅助的前景.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算精神病学是一种计算精神病学.
- 临床诊断 临床诊断 临床诊断
背景情况:
- 诊断精神分裂症可能具有挑战性,需要改进辅助工具.
- 常规血液检查提供了生物标志物的潜在来源,用于诊断精神分裂症.
研究的目的:
- 开发和验证基于机器学习的诊断工具,用于第一发精神分裂症.
- 确定关键的外周血液生化指标和预测精神分裂症的人口统计数据.
主要方法:
- 对180名第一发精神分裂症患者和214名对照患者的血液生化指标和人口统计数据的回顾性分析.
- 使用单变逻辑回归,Boruta和LASSO算法进行特征选择.
- 开发和评估七个机器学习模型,包括随机森林和物流回归,以AUC,灵敏度和特异性等性能指标.
主要成果:
- 确定了Arg,TP,ALP,HDL,UA和LDL作为重要的预测因素.
- 随机森林模型实现了1.00 (培训) 和0.877 (验证) 的AUC.
- 选择了一个多变量逻辑回归模型,以其可解释性和稳定性,并为临床使用构建了名ograms.
结论:
- 通过机器学习和常规血液指标,成功建立了精神分裂症的辅助诊断工具.
- 开发的后勤回归模型表现出良好的性能和实用性,作为精神分裂症的诊断辅助.
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