Journal of Forensic Medicine ›› 2024, Vol. 40 ›› Issue (1): 1-14.DOI: 10.12116/j.issn.1004-5619.2023.430311
• Original Articles • Next Articles
Yi-ming DONG1(), Chun-mei ZHAO1, Nian-nian CHEN1, Li LUO1, Zhan-peng LI1, Li-kai WANG1, Xiao-qian LI1, Ting-gan REN2, Cai-rong GAO1(
), Xiang-jie GUO1,3,4(
)
Received:
2023-03-23
Online:
2024-02-09
Published:
2024-02-25
Contact:
Cai-rong GAO, Xiang-jie GUO
CLC Number:
Yi-ming DONG, Chun-mei ZHAO, Nian-nian CHEN, Li LUO, Zhan-peng LI, Li-kai WANG, Xiao-qian LI, Ting-gan REN, Cai-rong GAO, Xiang-jie GUO. Visualization Analysis of Artificial Intelligence Literature in Forensic Research[J]. Journal of Forensic Medicine, 2024, 40(1): 1-14.
Add to citation manager EndNote|Ris|BibTeX
URL: http://www.fyxzz.cn/EN/10.12116/j.issn.1004-5619.2023.430311
序号 | 国家(地区) | 发文量/篇 | 中介中心性 |
---|---|---|---|
1 | 美国(USA) | 138 | 0.50 |
2 | 中国(P.R.CHINA) | 95 | 0.13 |
3 | 澳大利亚(AUSTRALIA) | 37 | 0.03 |
4 | 英国(ENGLAND) | 32 | 0.17 |
5 | 西班牙(SPAIN) | 30 | 0.13 |
6 | 印度(INDIA) | 28 | 0.11 |
7 | 德国(GERMANY) | 28 | 0.29 |
8 | 荷兰(NETHERLANDS) | 24 | 0.09 |
9 | 土耳其(TURKEY) | 23 | 0.01 |
10 | 法国(FRANCE) | 23 | 0.16 |
Tab. 1 Top 10 countries (regions) in terms of number of publications
序号 | 国家(地区) | 发文量/篇 | 中介中心性 |
---|---|---|---|
1 | 美国(USA) | 138 | 0.50 |
2 | 中国(P.R.CHINA) | 95 | 0.13 |
3 | 澳大利亚(AUSTRALIA) | 37 | 0.03 |
4 | 英国(ENGLAND) | 32 | 0.17 |
5 | 西班牙(SPAIN) | 30 | 0.13 |
6 | 印度(INDIA) | 28 | 0.11 |
7 | 德国(GERMANY) | 28 | 0.29 |
8 | 荷兰(NETHERLANDS) | 24 | 0.09 |
9 | 土耳其(TURKEY) | 23 | 0.01 |
10 | 法国(FRANCE) | 23 | 0.16 |
序号 | 机构 | 所属国家 | 发文量/篇 | 中介中心性 |
---|---|---|---|---|
1 | 司法鉴定科学研究院(Academy of Forensic Science) | 中国 | 19 | 0.01 |
2 | 科英布拉大学(University of Coimbra ) | 葡萄牙 | 14 | 0.01 |
3 | 四川大学(Sichuan University ) | 中国 | 11 | 0.00 |
4 | 西安交通大学(Xi’an Jiaotong University) | 中国 | 11 | 0.00 |
5 | 布拉格查理大学(Charles University in Prague) | 捷克共和国 | 11 | 0.01 |
6 | 密歇根州立大学(Michigan State University ) | 美国 | 10 | 0.00 |
7 | 苏黎世大学(University of Zurich) | 瑞士 | 9 | 0.01 |
8 | 公安部(The Ministry of Public Security) | 中国 | 9 | 0.01 |
9 | 蒙纳什大学(Monash University ) | 澳大利亚 | 8 | 0.00 |
10 | 阿姆斯特丹大学(University of Amsterdam) | 荷兰 | 8 | 0.00 |
Tab. 2 Top 10 institutions in terms of number of publications
序号 | 机构 | 所属国家 | 发文量/篇 | 中介中心性 |
---|---|---|---|---|
1 | 司法鉴定科学研究院(Academy of Forensic Science) | 中国 | 19 | 0.01 |
2 | 科英布拉大学(University of Coimbra ) | 葡萄牙 | 14 | 0.01 |
3 | 四川大学(Sichuan University ) | 中国 | 11 | 0.00 |
4 | 西安交通大学(Xi’an Jiaotong University) | 中国 | 11 | 0.00 |
5 | 布拉格查理大学(Charles University in Prague) | 捷克共和国 | 11 | 0.01 |
6 | 密歇根州立大学(Michigan State University ) | 美国 | 10 | 0.00 |
7 | 苏黎世大学(University of Zurich) | 瑞士 | 9 | 0.01 |
8 | 公安部(The Ministry of Public Security) | 中国 | 9 | 0.01 |
9 | 蒙纳什大学(Monash University ) | 澳大利亚 | 8 | 0.00 |
10 | 阿姆斯特丹大学(University of Amsterdam) | 荷兰 | 8 | 0.00 |
序号 | 期刊 | 发文量/篇 | IF | JCR 分区 | 共被引期刊 | 共被引频次 | IF | JCR 分区 |
---|---|---|---|---|---|---|---|---|
1 | Journal of Forensic Sciences | 73 | 1.717 | Q3 | Forensic Science International | 364 | 2.676 | Q2 |
2 | Forensic Science International | 71 | 2.676 | Q2 | Journal of Forensic Sciences | 333 | 1.717 | Q3 |
3 | International Journal of Legal Medicine | 54 | 2.791 | Q1 | International Journal of Legal Medicine | 245 | 2.791 | Q1 |
4 | Forensic Science International-Genetics | 52 | 4.453 | Q1 | American Journal of Physical Anthropology | 149 | 2.963 | Q1 |
5 | Journal of Forensic and Legal Medicine | 28 | 1.691 | Q3 | Journal of Forensic and Legal Medicine | 141 | 1.691 | Q3 |
6 | Legal Medicine | 20 | 2.017 | Q3 | Plos One | 119 | 3.752 | Q2 |
7 | PLoS One | 15 | 3.752 | Q2 | Forensic Science International-Genetics | 99 | 4.453 | Q1 |
8 | Forensic Science Medicine and Pathology | 14 | 2.456 | Q2 | Nature | 89 | 69.500 | Q1 |
9 | Australian Journal of Forensic Sciences | 13 | 1.210 | Q4 | Legal Medicine | 89 | 2.017 | Q3 |
10 | Scientific Reports | 12 | 4.996 | Q2 | Scientific Reports | 88 | 4.996 | Q2 |
Tab. 3 Top 10 journals in terms of number of publications and top 10 co-cited journals in terms of citation frequency
序号 | 期刊 | 发文量/篇 | IF | JCR 分区 | 共被引期刊 | 共被引频次 | IF | JCR 分区 |
---|---|---|---|---|---|---|---|---|
1 | Journal of Forensic Sciences | 73 | 1.717 | Q3 | Forensic Science International | 364 | 2.676 | Q2 |
2 | Forensic Science International | 71 | 2.676 | Q2 | Journal of Forensic Sciences | 333 | 1.717 | Q3 |
3 | International Journal of Legal Medicine | 54 | 2.791 | Q1 | International Journal of Legal Medicine | 245 | 2.791 | Q1 |
4 | Forensic Science International-Genetics | 52 | 4.453 | Q1 | American Journal of Physical Anthropology | 149 | 2.963 | Q1 |
5 | Journal of Forensic and Legal Medicine | 28 | 1.691 | Q3 | Journal of Forensic and Legal Medicine | 141 | 1.691 | Q3 |
6 | Legal Medicine | 20 | 2.017 | Q3 | Plos One | 119 | 3.752 | Q2 |
7 | PLoS One | 15 | 3.752 | Q2 | Forensic Science International-Genetics | 99 | 4.453 | Q1 |
8 | Forensic Science Medicine and Pathology | 14 | 2.456 | Q2 | Nature | 89 | 69.500 | Q1 |
9 | Australian Journal of Forensic Sciences | 13 | 1.210 | Q4 | Legal Medicine | 89 | 2.017 | Q3 |
10 | Scientific Reports | 12 | 4.996 | Q2 | Scientific Reports | 88 | 4.996 | Q2 |
序号 | 共被引文献 | 作者 | 期刊 | 共被引频次 | 中介 中心性 | 发表 年份 |
---|---|---|---|---|---|---|
1 | A review of sex estimation techniques during examination of skeletal remains in forensic anthropology casework | Kewal Krishan | Forensic Science International | 16 | 0.26 | 2016 |
2 | Deep learning | Yann LeCun | Nature | 12 | 0.01 | 2015 |
3 | Sex estimation from the tarsal bones in a Portuguese sample: A machine learning approach | David Navega | International Journal of Legal Medicine | 11 | 0.21 | 2015 |
4 | Chronological age prediction based on dna methylation: Massive parallel sequencing and random forest regression | Jana Naue | Forensic Science International-Genetics | 9 | 0.01 | 2017 |
5 | Odontometric sex assessment from logistic regression analysis | Ashith B Acharya | International Journal of Legal Medicine | 9 | 0.01 | 2011 |
6 | Dermatologist-level classification of skin cancer with deep neural networks | Andre Esteva | Nature | 9 | 0.01 | 2017 |
7 | DNA methylation-based forensic age prediction using artificial neural networks and next generation sequencing | Athina Vidaki | Forensic Science International-Genetics | 8 | 0.01 | 2017 |
8 | Sex estimation in forensic anthropology: Skull versus postcranial elements | M Katherine Spradley | Journal of Forensic Sciences | 8 | 0.12 | 2011 |
9 | Statistical sex determination from craniometrics: Comparison of linear discriminant analysis, logistic regression, and support vector machines | Frédéric Santos | Forensic Science International | 8 | 0.06 | 2014 |
10 | Artificial intelligence for sex determination of skeletal remains: Application of a deep learning artificial neural network to human skulls | James Bewes | Journal of Forensic and Legal Medicine | 8 | 0.23 | 2019 |
Tab. 4 Top 10 co-cited articles about artificial intelligence in forensic research
序号 | 共被引文献 | 作者 | 期刊 | 共被引频次 | 中介 中心性 | 发表 年份 |
---|---|---|---|---|---|---|
1 | A review of sex estimation techniques during examination of skeletal remains in forensic anthropology casework | Kewal Krishan | Forensic Science International | 16 | 0.26 | 2016 |
2 | Deep learning | Yann LeCun | Nature | 12 | 0.01 | 2015 |
3 | Sex estimation from the tarsal bones in a Portuguese sample: A machine learning approach | David Navega | International Journal of Legal Medicine | 11 | 0.21 | 2015 |
4 | Chronological age prediction based on dna methylation: Massive parallel sequencing and random forest regression | Jana Naue | Forensic Science International-Genetics | 9 | 0.01 | 2017 |
5 | Odontometric sex assessment from logistic regression analysis | Ashith B Acharya | International Journal of Legal Medicine | 9 | 0.01 | 2011 |
6 | Dermatologist-level classification of skin cancer with deep neural networks | Andre Esteva | Nature | 9 | 0.01 | 2017 |
7 | DNA methylation-based forensic age prediction using artificial neural networks and next generation sequencing | Athina Vidaki | Forensic Science International-Genetics | 8 | 0.01 | 2017 |
8 | Sex estimation in forensic anthropology: Skull versus postcranial elements | M Katherine Spradley | Journal of Forensic Sciences | 8 | 0.12 | 2011 |
9 | Statistical sex determination from craniometrics: Comparison of linear discriminant analysis, logistic regression, and support vector machines | Frédéric Santos | Forensic Science International | 8 | 0.06 | 2014 |
10 | Artificial intelligence for sex determination of skeletal remains: Application of a deep learning artificial neural network to human skulls | James Bewes | Journal of Forensic and Legal Medicine | 8 | 0.23 | 2019 |
序号 | 关键词 | 频次 | 中介中心性 |
---|---|---|---|
1 | forensic science | 95 | 0.05 |
2 | forensic anthropology | 82 | 0.19 |
3 | machine learning | 78 | 0.04 |
4 | forensic identification | 72 | 0.09 |
5 | sex estimation | 66 | 0.28 |
6 | age estimation | 65 | 0.13 |
7 | sexual dimorphism | 64 | 0.25 |
8 | classification | 46 | 0.11 |
9 | logistic regression | 42 | 0.21 |
10 | deep learning | 41 | 0.03 |
11 | population | 32 | 0.13 |
12 | discriminant function analysis | 31 | 0.14 |
13 | forensic dentistry | 28 | 0.08 |
14 | accuracy | 23 | 0.16 |
15 | artificial intelligence | 22 | 0.00 |
16 | convolutional neural network | 20 | 0.01 |
17 | personal identification | 19 | 0.06 |
18 | death | 18 | 0.28 |
19 | forensic anthropology population data | 18 | 0.01 |
20 | support vector machine | 18 | 0.01 |
Tab. 5 Top 20 keywords of artificial intelligence in forensic research
序号 | 关键词 | 频次 | 中介中心性 |
---|---|---|---|
1 | forensic science | 95 | 0.05 |
2 | forensic anthropology | 82 | 0.19 |
3 | machine learning | 78 | 0.04 |
4 | forensic identification | 72 | 0.09 |
5 | sex estimation | 66 | 0.28 |
6 | age estimation | 65 | 0.13 |
7 | sexual dimorphism | 64 | 0.25 |
8 | classification | 46 | 0.11 |
9 | logistic regression | 42 | 0.21 |
10 | deep learning | 41 | 0.03 |
11 | population | 32 | 0.13 |
12 | discriminant function analysis | 31 | 0.14 |
13 | forensic dentistry | 28 | 0.08 |
14 | accuracy | 23 | 0.16 |
15 | artificial intelligence | 22 | 0.00 |
16 | convolutional neural network | 20 | 0.01 |
17 | personal identification | 19 | 0.06 |
18 | death | 18 | 0.28 |
19 | forensic anthropology population data | 18 | 0.01 |
20 | support vector machine | 18 | 0.01 |
1 | 严律南. 人工智能在医学领域应用的现状与展望[J].中国普外基础与临床杂志,2018,25(5):513-514. doi:10.7507/1007-9424.201804033 . |
YAN L N. Present situation and prospect of application of artificial intelligence in medical field[J]. Zhongguo Puwai Jichu Yu Linchuang Zazhi,2018,25(5):513-514. | |
2 | 史忠植. 人工智能[M].北京: 机械工业出版社,2016:16. |
SI Z Z. Artificial intelligence[M]. Beijing: China Machine Press,2016:16. | |
3 | 刘志勇,张更谦,严江伟. 人工智能在法医学中的应用与展望[J].刑事技术,2019,44(5):383-387. doi:10.16467/j.1008-3650.2019.05.002 . |
LIU Z Y, ZHANG G Q, YAN J W. Forensic applicability of artificial intelligence and prospect[J]. Xingshi Jishu,2019,44(5):383-387. | |
4 | 方雅婷,兰琼,解通,等. 人工智能技术时代法医学科面临的新机遇与挑战[J].法医学杂志,2020,36(1):77-85. doi:10.12116/j.issn.1004-5619.2020.01.016 . |
FANG Y T, LAN Q, XIE T, et al. New opportunities and challenges for forensic medicine in the era of artificial intelligence technology[J]. Fayixue Zazhi,2020,36(1):77-85. | |
5 | 梁国强. 国内文献计量学综述[J].科技文献信息管理,2013,27(4):58-59,62. |
LIANG G Q. Review of domestic bibliometrics[J]. Keji Wenxian Xinxi Guanli,2013,27(4):58-59,62. | |
6 | 李贺,袁翠敏,李亚峰. 基于文献计量的大数据研究综述[J].情报科学,2014,32(6):148-155. doi:10.13833/j.cnki.is.2014.06.026 . |
LI H, YUAN C M, LI Y F. A review of big data research based on bibliometrics[J]. Qingbao Kexue,2014,32(6):148-155. | |
7 | 刘则渊,陈悦,侯海燕,等. 科学知识图谱:方法与应用[M]. 北京:人民出版社,2008:62. |
LIU Z Y, CHEN Y, HOU H Y,et al. Mapping knowledge domains:methods and application[M]. Beijing: People’s Publishing House,2008:62. | |
8 | 陈悦,陈超美,刘则渊, 等. CiteSpace知识图谱的方法论功能[J].科学学研究,2015,33(2):242-253. doi:10.16192/j.cnki.1003-2053.2015.02.009 . |
CHEN Y, CHEN C M, LIU Z Y, et al. The metho-dology function of CiteSpace mapping knowledge domains[J]. Kexuexue Yanjiu,2015,33(2):242-253. | |
9 | LU X J, LI J, WEI X, et al. A novel method for determining postmortem interval based on the metabolomics of multiple organs combined with ensemble learning techniques[J]. Int J Legal Med,2023,137(1):237-249. doi:10.1007/s00414-022-02844-8 . |
10 | LIN H C, WANG Z Y, LUO Y W, et al. Post-mortem evaluation of the pathological degree of myocardial infarction by Fourier transform infrared microspectroscopy[J]. Spectrochim Acta A Mol Biomol Spectrosc,2022,268:120630. doi:10.1016/j.saa.2021.120630 . |
11 | LIN H C, LUO Y W, SUN Q R, et al. Determination of causes of death via spectrochemical analysis of forensic autopsies-based pulmonary edema fluid samples with deep learning algorithm[J]. J Biophotonics,2020,13(4):e201960144. doi:10.1002/jbio.201960144 . |
12 | ZHANG J, LI B, WANG Q, et al. Application of Fourier transform infrared spectroscopy with chemometrics on postmortem interval estimation based on pericardial fluids[J]. Sci Rep,2017,7:18013. doi:10.1038/s41598-017-18228-7 . |
13 | ZHOU Y Y, ZHANG J, HUANG J, et al. Digital whole-slide image analysis for automated diatom test in forensic cases of drowning using a convolutional neural network algorithm[J]. Forensic Sci Int,2019,302:109922. doi:10.1016/j.forsciint.2019. 109922 . |
14 | YU W M, XIANG Q Q, HU Y C, et al. An improved automated diatom detection method based on YOLOv5 framework and its preliminary study for taxonomy recognition in the forensic diatom test[J]. Front Microbiol,2022,13:963059. doi:10.3389/fmicb.2022.963059 . |
15 | FAN F, KE W C, WU W, et al. Automatic human identification from panoramic dental radiographs using the convolutional neural network[J]. Forensic Sci Int,2020,314:110416. doi:10.1016/j.forsciint.2020.110416 . |
16 | LI Y, HUANG Z Z, DONG X A, et al. Forensic age estimation for pelvic X-ray images using deep learning[J]. Eur Radiol,2019,29(5):2322-2329. doi:10.1007/s00330-018-5791-6 . |
17 | NAVEGA D, COSTA E, CUNHA E. Adult skeletal age-at-death estimation through deep random neural networks: A new method and its computational analysis[J]. Biology,2022,11(4):532. doi:10.3390/biology11040532 . |
18 | NAVEGA D, COELHO J D, CUNHA E, et al. DXAGE: A new method for age at death estimation based on femoral bone mineral density and artificial neural networks[J]. J Forensic Sci,2018,63(2):497-503. doi:10.1111/1556-4029.13582 . |
19 | ČECHOVÁ M, DUPEJ J, BRŮŽEK J, et al. Sex estimation using external morphology of the frontal bone and frontal sinuses in a contemporary Czech population[J]. Int J Legal Med,2019,133(4):1285-1294. doi:10.1007/s00414-019-02063-8 . |
20 | POŚPIECH E, KARŁOWSKA-PIK J, ZIEMKIE-WICZ B, et al. Further evidence for population specific differences in the effect of DNA markers and gender on eye colour prediction in forensics[J]. Int J Legal Med,2016,130(4):923-934. doi:10.1007/s00414-016-1388-2 . |
21 | POŚPIECH E, KARŁOWSKA-PIK J, MARCIŃS-KA M, et al. Evaluation of the predictive capacity of DNA variants associated with straight hair in Europeans[J]. Forensic Sci Int Genet,2015,19:280-288. doi:10.1016/j.fsigen.2015.09.004 . |
22 | HOFMANN L A, LAU S, KIRCHEBNER J. Advantages of machine learning in forensic psychiatric research—Uncovering the complexities of aggressive behavior in schizophrenia[J]. Appl Sci (Basel),2022,12(2):819. doi:10.3390/app12020819 . |
23 | PATTERSON A, SONNWEBER M, LAU S, et al. Schizophrenia and substance use disorder: Characteristics of coexisting issues in a forensic setting[J]. Drug Alcohol Depend,2021,226:108850. doi:10.1016/j.drugalcdep.2021.108850 . |
24 | 王大阜. 科学知识图谱:工具、方法与应用[M].北京:人民邮电出版社,2023:54-55. |
WANG D F. Scientific knowledge mapping: Tools, methods and applications[M]. Beijing:POSTS & TELECOM PRESS,2023:54-55. | |
25 | NOVAK L, SCHULTZ J J, MCINTYRE M. Determining sex of the posterior ilium from the Robert J. Terry and William M. Bass collections[J]. J Forensic Sci,2012,57(5):1155-1160. doi:10.1111/j.1556-4029.2012.02122.x |
26 | RAGHAVENDRA BABU Y P, KANCHAN T, ATTI-KU Y, et al. Sex estimation from foramen magnum dimensions in an Indian population[J]. J Forensic Leg Med,2012,19(3):162-167. doi:10.1016/j.jflm.2011.12.019 . |
27 | CHANDRAKANTH H V, KANCHAN T, KRIS-HAN K. Osteometric analysis for sexing of modern sternum -- An autopsy study from South India[J]. Leg Med (Tokyo),2014,16(6):350-356. doi:10. 1016/j.legalmed.2014.07.007 . |
28 | GAO H J, GENG G H, YANG W. Sex determination of 3D skull based on a novel unsupervised learning method[J]. Comput Math Methods Med,2018,2018:4567267. doi:10.1155/2018/4567267 . |
29 | LI Y, NIU C Q, WANG J, et al. A fully automated sex estimation for proximal femur X-ray images through deep learning detection and classification[J]. Leg Med (Tokyo),2022,57:102056. doi:10.1016/j.legalmed.2022.102056 . |
30 | THURZO A, KOSNÁČOVÁ H S, KURILOVÁ V, et al. Use of advanced artificial intelligence in forensic medicine, forensic anthropology and clinical anatomy[J]. Healthcare,2021,9(11):1545. doi:10.3390/healthcare9111545 . |
31 | ŠTEPANOVSKÝ M, IBROVÁ A, BUK Z, et al. Novel age estimation model based on development of permanent teeth compared with classical approach and other modern data mining methods[J]. Forensic Sci Int,2017,279:72-82. doi:10.1016/j.forsciint.2017.08.005 . |
32 | LI Y, WANG J, LIANG W B, et al. CR-GAN: Automatic craniofacial reconstruction for personal identification[J]. Pattern Recognition,2022,124:108400. doi:10.1016/j.patcog.2021.108400 . |
33 | JOSHI S V, KANPHADE R D. Deep learning based person authentication using hand radiographs: A forensic approach[J]. IEEE Access,2020,8:95424-95434. doi:10.1109/ACCESS.2020.2995788 . |
34 | 王琪, 林汉成, 徐纪茹, 等. 死亡时间推断最新研究与展望[J].法医学杂志,2018,34(5):459-467. doi:10.12116/j.issn.1004-5619.2018.05.002 . |
WANG Q, LIN H C, XU J R, et al. Current research and prospects on postmortem interval estimation[J]. Fayixue Zazhi,2018,34(5):459-467. | |
35 | ZHAO X C, ZHONG Z T, HUA Z. Estimation of the post‐mortem interval by modelling the changes in oral bacterial diversity during decomposition[J]. J Appl Microbiol,2022,133:3451-3464. doi:10.1111/jam.15771 . |
36 | ZHANG J, WEI X, HUANG J, et al. Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectral prediction of postmortem interval from vitreous humor samples[J]. Anal Bioanal Chem,2018,410(29):7611-7620. doi:10.1007/s00216-018-1367-1 . |
37 | ZHANG X Y, BAI Y, NGANDO F J, et al. Predicting the weathering time by the empty puparium of Sarcophaga peregrina (diptera: Sarcophagidae) with the ANN models[J]. Insects,2022,13(9):808. doi:10.3390/insects13090808 . |
38 | KASZUBINSKI S F, PECHAL J L, SMILES K, et al. Dysbiosis in the dead: Human postmortem microbiome beta-dispersion as an indicator of manner and cause of death[J]. Front Microbiol,2020,11:555347. doi:10.3389/fmicb.2020.555347 . |
39 | EBERT L C, HEIMER J, SCHWEITZER W, et al. Automatic detection of hemorrhagic pericardial effusion on PMCT using deep learning -- A feasibility study[J]. Forensic Sci Med Pathol,2017,13(4):426-431. doi:10.1007/s12024-017-9906-1 . |
40 | LOU J Q, CHEN H Y, HUANG S N, et al. Update on risk factors and biomarkers of sudden unexplained cardiac death[J]. J Forensic Leg Med,2022,87:102332. doi:10.1016/j.jflm.2022.102332 . |
41 | ZHANG F Y, WANG L L, DONG W W, et al. A preliminary study on early postmortem submersion interval (PMSI) estimation and cause-of-death discrimination based on nontargeted metabolomics and machine learning algorithms[J]. Int J Legal Med,2022,136(3):941-954. doi:10.1007/s00414-022-02783-4 . |
42 | CAO J, LI J, GU Z, et al. Combined metabolomics and machine learning algorithms to explore metabolic biomarkers for diagnosis of acute myocardial ischemia[J]. Int J Legal Med,2023,137(1):169-180. doi:10.1007/s00414-022-02816-y . |
43 | GARLAND J, HU M, DUFFY M, et al. Classifying microscopic acute and old myocardial infarction using convolutional neural networks[J]. Am J Forensic Med Pathol,2021,42(3):230-234. doi:10.1097/PAF. 0000000000000672 . |
44 | MUJTABA G, SHUIB L, RAJ R G, et al. Prediction of cause of death from forensic autopsy reports using text classification techniques: A comparative study[J]. J Forensic Leg Med,2018,57:41-50. doi:10.1016/j.jflm.2017.07.001 . |
45 | ROWBOTHAM S K, BLAU S, HISLOP-JAMBRICH J, et al. An assessment of the skeletal fracture patterns resulting from fatal high (˃3 m) free falls[J]. J Forensic Sci,2019,64(1):58-68. doi:10.1111/1556-4029.13803 . |
46 | ROWBOTHAM S K, BLAU S, HISLOP-JAMB-RICH J, et al. An anthropological examination of the types of skeletal fractures resulting from fatal high (˃3 m) free falls[J]. J Forensic Sci,2019,64(2):375-384. doi:10.1111/1556-4029. 13887 . |
47 | REN G H, ZOU D H, HUANG P, et al. Identifying diffuse axonal injury by matrix-assisted laser desorption/ionization time-of-flight[J]. Am J Forensic Med Pathol,2016,37(4):279-283. doi:10.1097/paf.0000000000000275 . |
48 | VOLGIN L, TAYLOR D, BRIGHT J A, et al. Validation of a neural network approach for STR typing to replace human reading[J]. Forensic Sci Int Genet,2021,55:102591. doi:10.1016/j.fsigen.20 21.102591 . |
49 | ALOTAIBI H, ALSOLAMI F, ABOZINADAH E, et al. TAWSEEM: A deep-learning-based tool for estimating the number of unknown contributors in DNA profiling[J]. Electronics,2022,11(4):548. doi:10.3390/electronics11040548 . |
50 | KATSARA M A, BRANICKI W, WALSH S, et al. Evaluation of supervised machine-learning methods for predicting appearance traits from DNA[J]. Forensic Sci Int Genet,2021,53:102507. doi:10.1016/j.fsigen.2021.102507 . |
51 | DEVRANOGLU D, TAVACI I, FILOGLU G, et al. Effect of type of degraded DNA samples on human eye color prediction[J]. Pak J Zool,2021,53(4):1201-1209. doi:10.17582/journal.pjz/20200310120332 . |
52 | HWA H L, WU M Y, LIN C P, et al. A single nucleotide polymorphism panel for individual identification and ancestry assignment in Caucasians and four East and Southeast Asian populations using a machine learning classifier[J]. Forensic Sci Med Pathol,2019,15(1):67-74. doi:10.1007/s12024-018-0071-y . |
53 | VIDAKI A, BALLARD D, ALIFERI A, et al. DNA methylation-based forensic age prediction using artificial neural networks and next generation sequencing[J]. Forensic Sci Int Genet,2017,28:225-236. doi:10.1016/j.fsigen.2017.02.009 . |
54 | 喻小兰. 中外比较视角下青少年犯罪预防研究[J].法制博览,2022(19):8-10. doi:10.3969/j.issn.2095-4379.2022.19.003 |
YU X L. A comparative study of juvenile delinquency prevention between China and foreign countries[J]. Fazhi Bolan,2022(19):8-10. | |
55 | STREUN GABRIEL L, STEUER ANDREA E, POETZSCH SANDRA N, et al. Towards a new qualitative screening assay for synthetic cannabinoids using metabolomics and machine learning[J]. Clin Chem,2022,68(6):848-855. doi:10.1093/clinchem/hvac045 . |
56 | KRANENBURG R F, VERDUIN J, WEESEPOEL Y, et al. Rapid and robust on-scene detection of cocaine in street samples using a handheld near-infrared spectrometer and machine learning algorithms[J]. Drug Test Anal,2020,12(10):1404-1418. doi:10.1002/dta.2895 . |
57 | WATTS D, MOULDEN H, MAMAK M, et al. Predicting offenses among individuals with psychiatric disorders -- A machine learning approach[J]. J Psychiatr Res,2021,138:146-154. doi:10.1016/j.jpsychires.2021.03.026 . |
58 | TESLI N, BELL C, HJELL G, et al. The age of violence: Mapping brain age in psychosis and psychopathy[J]. Neuroimage Clin,2022,36:103181. doi:10.1016/j.nicl.2022.103181 . |
[1] | Chen-shan LÜ, Yi-xuan CAO, Xiao-xi MU, Hai-yan CUI, Tao WANG, Zhi-wen WEI, Ke-ming YUN, Meng HU. Rapid Screening of 34 Emerging Contaminants in Surface Water by UHPLC-Q-TOF-MS [J]. Journal of Forensic Medicine, 2024, 40(1): 30-36. |
[2] | Qing-wei FAN, Ling LI, Hui-ling YANG, Ting-ting DENG, Dong-dong XU, Yun WANG, Bing DU, Jiang-wei YAN. A Bibliometric and Visual Analysis of the Current Status and Trends of Forensic Mixed Stain Research [J]. Journal of Forensic Medicine, 2024, 40(1): 20-29. |
[3] | He-ying CHENG, Yun-ge ZHANG, Yan CHEN, Sun YIN, Ming LÜ, Chun-xiao LI. Correlation Analysis and Comparison of Adult CE-Chirp ABR Response Threshold and Pure Tone Hearing Threshold [J]. Journal of Forensic Medicine, 2024, 40(1): 15-19. |
[4] | Yong YU, Ying-jie WANG, Yun-fei JIA, Bao-jing HUANG, Song-yue HE, Chuan-chuan LIU. Forensic Identification and Evaluation of 25 Obstetric Brachial Plexus Palsy Medical Damage Cases [J]. Journal of Forensic Medicine, 2024, 40(1): 43-49. |
[5] | Wei-guang YU, Qiang HE, Zheng-di WANG, Cheng-jun TIAN, Jin-kai WANG, Qian ZHENG, Fei REN, Chao ZHANG, You-mei WANG, Peng XU, Zhi-wen WEI, Ke-ming YUN. Toxicokinetics of MDMA and Its Metabolite MDA in Rats [J]. Journal of Forensic Medicine, 2024, 40(1): 37-42. |
[6] | Xing HAN, Xin LIU, Ming-luo DU, Ruo-lun XU, Jia-rong LI, Chao LIU, Wei-guo LIU. UPLC-MS/MS Method for Detection of Etomidate and Its Metabolite Etomidate Acid Quantity in Blood [J]. Journal of Forensic Medicine, 2023, 39(6): 564-570. |
[7] | Ao HUANG, Shu-bo WEN, Qian-qian KONG, Zhen-min ZHAO, Xi-ling LIU. Proteomic Difference Analysis of Whole Blood and Bloodstains [J]. Journal of Forensic Medicine, 2023, 39(6): 549-556. |
[8] | Xing-yu MA, Hao CHENG, Zhong-duo ZHANG, Ye-ming LI, Dong ZHAO. Research Progress of Metabolomics Techniques Combined with Machine Learning Algorithm in Wound Age Estimation [J]. Journal of Forensic Medicine, 2023, 39(6): 596-600. |
[9] | Ying LI, Yong YU, Xing-hua KOU, Zhan-long HAN. Forensic Analysis of Eighteen Tubal Pregnancy-Related Medical Damage [J]. Journal of Forensic Medicine, 2023, 39(6): 571-578. |
[10] | Hong-xia HAO, Jie-min CHEN, Rong-rong WANG, Xiao-ying YU, Meng WANG, Zhi-lu ZHOU, Yan-liang SHENG, Wen-tao XIA. The Value of VR-PVEP in Objective Evaluation of Monocular Refractive Visual Impairment [J]. Journal of Forensic Medicine, 2023, 39(4): 382-387. |
[11] | Jie BAI, Jing SUN, Xiao-guang CHENG, Fan LIU, Hua LIU, Xu WANG. Construction and Application of Rib Fracture Diagnosis Model Based on YOLOv3 Algorithm [J]. Journal of Forensic Medicine, 2023, 39(4): 343-349. |
[12] | Fei FAN, Juan WU, Zhen-hua DENG. Application Progress of Objective Audiological Detection Techniques in Forensic Clinical Medicine [J]. Journal of Forensic Medicine, 2023, 39(4): 360-366. |
[13] | Jian XIANG, Xu WANG, Li-li YU, Kang-jia JIN, Ying-kai YANG. Objective Assessment of Visual Field Defects Caused by Optic Chiasm and Its Posterior Visual Pathway Injury [J]. Journal of Forensic Medicine, 2023, 39(4): 350-359. |
[14] | Zhang-ming GAO, Jing-yu SHI, Hao ZENG, Xue-jun ZHANG. Rapid Determination of Bucinnazine in Blood by UPLC-MS/MS [J]. Journal of Forensic Medicine, 2023, 39(4): 388-392. |
[15] | Yu-qi CAO, Yan SHI, Ping XIANG, Yin-long GUO. Research Progress on Machine Learning Assisted Non-Targeted Screening Strategy for Identification of Fentanyl Analogs [J]. Journal of Forensic Medicine, 2023, 39(4): 406-416. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||