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半监督学习与PMI估计的动态分类器选择:一项动物研究

Jian Li1, Yan-Juan Wu1, Xing-Yu Lu1

  • 1School of Forensic Medicine, Shanxi Medical University, No. 98, University Street, Wujinshan Town, Yuci District, Jinzhong City, Shanxi Province, 030604, China; Shanxi Key Laboratory of Forensic Medicine, Jinzhong, 030600, Shanxi, China.

Journal of forensic and legal medicine
|December 17, 2025
PubMed
概括

具有动态分类器选择 (SSL-DCS) 的半监督学习改进了用于估计死后间隔 (PMI) 的机器学习模型. 这种方法有效地利用有限的样本,提高了法医科学应用的准确性.

关键词:
在 DCS 中使用 DCS 系统.法医医学法医学的研究.验尸后的时间间隔.半监督学习 半监督学习

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科学领域:

  • 法医科学 法医科学 法医科学
  • 机器学习 机器学习
  • 分子生物学分子生物学

背景情况:

  • 机器学习用于死后间隔 (PMI) 估计面临的挑战是由于有限的标记样本.
  • 不确定的PMI样本降低了模型的有效性,并加剧了法医调查中的样本稀缺性.

研究的目的:

  • 用骨肌样本评估PMI估计的监督学习 (SL) 和半监督学习 (SSL) 模型.
  • 通过动态分类器选择 (DCS) 增强SL和SSL模型,以提高性能.

主要方法:

  • 使用了具有已知和未知的PMI的老鼠骨肌肉样本.
  • 与SL和SSL模型的有效性进行比较,无论是带有DCS还是没有DCS.
  • 使用AUC和R平方等指标评估模型性能.

主要成果:

  • 在PMI预测中,SSL-DCS显著超过SL-DCS.
  • 通过SSL-DCS实现了0.89的AUC和0.93的R平方,估计了0-9天内死亡的时间.
  • 该研究表明,预测效率提高,稀缺样本的利用率提高.

结论:

  • SSL-DCS为基于机器学习的PMI估计提供了一种卓越的方法.
  • 这种方法有效地解决了法医科学中有限数据的挑战.
  • SSL-DCS范式显示出在其他生物医学领域的应用潜力.