Research Progress of Metabolomics Techniques Combined with Machine Learning Algorithm in Wound Age Estimation.
10.12116/j.issn.1004-5619.2022.421105
- Author:
Xing-Yu MA
1
;
Hao CHENG
2
;
Zhong-Duo ZHANG
2
;
Ye-Ming LI
1
;
Dong ZHAO
1
Author Information
1. Collaborative Innovation Center of Judicial Civilization, Key Laboratory of Evidence Science, Ministry of Education, China University of Political Science and Law, Beijing 100088, China.
2. Department of Forensic Pathology, School of Forensic Medicine, China Medical University, Shenyang 110122, China.
- Publication Type:Journal Article
- Keywords:
forensic medicine;
machine learning;
metabolomics;
review;
wound age estimation
- MeSH:
Algorithms;
Machine Learning;
Forensic Medicine;
Metabolomics;
Big Data
- From:
Journal of Forensic Medicine
2023;39(6):596-600
- CountryChina
- Language:English
-
Abstract:
Wound age estimation is the core content in the practice of forensic medicine. Accurate estimation of wound age is a scientific question that needs to be urgently solved by forensic scientists at home and abroad. Metabolomics techniques can effectively detect endogenous metabolites produced by internal or external stimulating factors and describe the dynamic changes of metabolites in vivo. It has the advantages of strong operability, high detection efficiency and accurate quantitative results. Machine learning algorithm has special advantages in processing high-dimensional data sets, which can effectively mine biological information and truly reflect the physiological, disease or injury state of the body. It is a new technical means for efficiently processing high-throughput big data. This paper reviews the status and advantages of metabolomic techniques combined with machine learning algorithm in the research of wound age estimation, and provides new ideas for this research.
