探索小物理异常的初级和相互作用影响:使用可解释的机器学习算法开发和验证用于早期精神分裂症的预测模型
Chih-Wei Lin1,2, Jin-Jia Lin3, Huai-Hsuan Tseng4
1Institute of Clinical Medicine, College of Medicine, National Cheng Kung University, Tainan 704302, Taiwan.
Schizophrenia bulletin
|April 3, 2025
概括
轻微的身体异常,特别是口腔异常,是早期精神分裂症 (EOS) 的关键指标. 使用这些标记物的机器学习模型有效预测EOS风险,帮助早期干预.
科学领域:
- 神经发育障碍 神经发育障碍
- 精神病学研究精神病学研究
- 生物医学信息学是生物医学信息学.
背景情况:
- 轻微的身体异常 (MPAs) 是产前神经发育标志物.
- MPA可能是早期精神分裂症 (EOS) 的重要指标.
研究的目的:
- 调查EOS中MPAs的主要影响和相互作用影响.
- 开发和验证一个可解释的机器学习模型,用于使用MPA进行EOS预测.
主要方法:
- 使用随机森林 (varSelRF) 和递归特征消除 (RFE) 进行MPA变量选择.
- 开发了使用基于选定的MPA的机器学习算法的预测模型.
- 包括549名精神分裂症患者 (193名EOS,356名AOS) 和420名健康对照 (HC).
主要成果:
- 被识别为显著的MPAs的口腔异常与EOS的相互作用效应.
- 机器学习模型显示,EOS与HC (AUC 0.85-0.93) 的区别优于AOS与HC (AUC 0.80-0.87) 的区别.
- 模型还将EOS与AOS区分开来 (AUC为0.67-0.77).
结论:
- 开发了早期精神分裂症的风险预测模型.
- 该模型作为临床决策支持系统,用于早期检测和干预.
- 突出了MPAs在识别患有EOS高风险的个体方面的临床实用性.
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