基于非向代谢学和机器学习算法对低温死亡的死后间隔 (PMI) 估计等级策略的研究
Ruijuan Li1, Ribin Xu1, Yong Liu1
1Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, No. 13 Hangkong Road, Wuhan 430030, China.
Legal medicine (Tokyo, Japan)
|August 19, 2025
概括
在低温死亡中估计死后间隔是具有挑战性的. 这项研究使用大鼠的代谢学和机器学习来开发一种新的分级诊断策略,以准确估计死亡时间.
科学领域:
- 法医科学 法医科学 法医科学
- 代谢学 代谢学 代谢学
- 机器学习 机器学习
背景情况:
- 在低温病例中估计死后间隔 (PMI) 在法医调查中提出了重大挑战.
- 死亡后的代谢变化为PMI估计提供了潜在的生物标志物,动物模型有助于研究.
研究的目的:
- 研究非向代谢学和机器学习在低温死亡中估计PMI的实用性.
- 在死后48小时内开发和验证PMI估计的预测模型.
主要方法:
- 使用超高性能液态染色学-并列质谱法 (UPLC-MS/MS) 在低温引起的老鼠死亡中,分析状肌肉代谢.
- 在48种差异代谢物上应用6种机器学习算法 (KNN,DT,SVM,RF,LR,GNB) 来预测PMI.
- 模型训练,交叉验证,参数调整和使用混矩阵和ROC曲线分析的性能评估.
主要成果:
- 一些机器学习模型,包括KNN,LR和SVM,在预测PMI层方面表现出很高的准确性.
- 性能最好的模型显示出最小的错误,KNN,SVM和LR在测试样本中只产生了一个错误.
- 差异性代谢物被确定为潜在的生物标志物,用于区分死后期内更细微的时间间隔.
结论:
- 基于新陈代谢数据和机器学习的诊断分级的新策略显示,对低温死亡的PMI准确估计有希望.
- 这种方法为法医实践在具有挑战性的低温病例中确定死亡以来的时间提供了潜在的进步.
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