从微生物数据到法医洞察力:系统审查用于PMI估计的机器学习模型
Abdulkreem Abdullah Al-Juhani1, Arwa Mohammad Gaber2, Rodan Mahmoud Desoky2
1Department of Surgery, King Abdulaziz University Hospital, Jeddah, Saudi Arabia. Asurgeon1@outlook.com.
Forensic science, medicine, and pathology
|April 21, 2025
概括
法医科学在机器学习和微生物组分析方面取得了进展,以准确地估计死后间隔 (PMI). 随机森林模型显示出希望,但标准化是可靠PMI预测的关键.
科学领域:
- 法医科学 法医科学 法医科学
- 微生物学 微生物学
- 生物信息学是一种生物信息学.
背景情况:
- 传统的死后间隔 (PMI) 估计方法由于环境变化和人为错误而面临限制.
- 新兴的分子和微生物技术为PMI确定提供了更高的准确性.
- 机器学习 (ML) 与微生物数据的整合显示了提高PMI估计可靠性的潜力.
研究的目的:
- 系统地审查和比较基于微生物组的PMI预测方法.
- 分析各种机器学习技术在不同器官和环境中的性能.
- 为PMI估计确定最有效的ML模型和微生物数据类型.
主要方法:
- 在主要的科学数据库 (PubMed,Scopus,Web of Science,IEEE,Cochrane图书馆) 进行全面的文献搜索,截至2024年9月.
- 由两个独立审稿人进行系统的数据提取,重点关注研究细节,样本类型,PMI范围,ML算法和绩效指标.
- 基于错误指标 (例如,平均绝对错误) 和解释差异的ML模型的排名和分析.
主要成果:
- 随机森林 (RF) 模型在PMI估计中表现出高准确性,报告的平均绝对误差 (MAE) 低至6.93小时 (Wang, 2024).
- 使用土壤样本和16S rRNA数据与射频模型的研究,在1.5天左右实现了MAE (Yang, 2023; Belk, 2018).
- 神经网络也显示出有效性,其中一项研究报告MAE为14.483小时 (Liu, 2020).
结论:
- 机器学习,特别是与16SrRNA和土壤微生物数据相结合的射频模型,显示出对准确PMI估计的重大前景.
- 需要进一步的研究来标准化参数,并在不同的法医环境中验证这些模型.
- 基于微生物组的方法比传统的PMI估计技术有了显著的进步.
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