可解释的非法药物滥用预测使用血液学差异
Aijun Chen1, Yinchu Shen1, Yu Xu2
1College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou, 310018, China.
Scientific reports
|August 7, 2025
概括
这项研究开发了一种机器学习模型,使用血液检测结果来预测非法药物使用 (IDU). 可解释模型准确地识别了IDU,有助于临床查和器官功能评估.
科学领域:
- 生物医学信息学 生物医学信息学
- 临床化学 临床化学
- 医疗保健中的机器学习
背景情况:
- 非法药物使用 (IDU) 带来了重大的公共卫生挑战.
- 准确和早期识别注射药物使用是及时干预和治疗的关键.
- 血液学参数为预测IDU提供了潜在的生物标志物,但需要强大的分析模型.
研究的目的:
- 开发和验证可靠和可解释的机器学习 (ML) 模型,用于预测非法药物使用 (IDU).
- 确定有助于预测IDU的关键血液学特征.
- 评估预测模型对初步IDU查的临床实用性.
主要方法:
- 利用了286名非法毒品使用者 (IDUr) 和302名非使用者 (n-IDUr) 的血液学数据.
- 对比了八个ML算法的性能来预测IDU.
- 开发了一个可解释的光梯度增强机 (LGB) 模型,使用13个选定的功能.
- 为了模型的可解释性,使用了夏普利添加式解释 (SHAP).
主要成果:
- 可解释的LGB模型实现了高预测准确性,在内部验证中,曲线下的面积 (AUC) 为0.925,在外部验证中为0.915.
- 发现的关键预测特征包括化物 (Cl),β-基酸盐 (BHB) 和离子间隙 (AG).
- 这些特征与脏,肝脏和甲状腺功能有关,表明IDUr的潜在器官功能障碍.
结论:
- 一个可解释的ML模型有效地根据血液学参数预测非法药物使用情况.
- 化物,BHB和离子间隙是IDU预测的重要指标.
- 该模型有可能在初步IDU查和指导进一步的器官特异性检查中进行临床应用.
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