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基于中国知网数据库(1990 - 2020年)的死后间隔时间估计文献信息可视化分析

Visualization of Literature Information on Postmortem Interval Estimation Indexed by CNKI Database from 1990 to 2020.

作者信息

Lin Ling-Xiao, Xin Guo-Bin, Kong Jiang-Wei, Zhai Chuang-Yan

机构信息

School of Forensic Medicine, Southern Medical University, Guangzhou 510515, China.

Key Laboratory of Forensic Toxicology, Ministry of Public Security, People's Republic of China, Beijing 100192, China.

出版信息

Fa Yi Xue Za Zhi. 2022 Oct 25;38(5):584-588. doi: 10.12116/j.issn.1004-5619.2020.400902.

Abstract

OBJECTIVES

To explore the development process of the postmortem interval (PMI) research in China from January 1990 to August 2020, research hotspots in different periods, authors and cooperation between institutions, and to provide a reference for the better development of PMI inference research through the visualization of the literature information of the PMI estimation research indexed in China National Knowledge Infrastructure (CNKI).

METHODS

The information visualization analysis software CiteSpace 5.7.R1 was used to carry out big data analysis on hotspots, high-frequency keywords, authors, institutions and other information in the research literature on PMI inference from January 1990 to August 2020 indexed in CNKI.

RESULTS

The peak time of publication of PMI was from 2006 to 2010 with 114 articles. In keyword co-occurrence network, the effective hot words were forensic entomology, DNA content analysis and some emerging words such as artificial intelligence and big data. In the cooperation network of institutions, the high-frequency institutions were mainly the scientific research institutions. The author cooperation network showed a trend of co-aggregation and multi-cooperation.

CONCLUSIONS

With the development of science and technology, the research on PMI estimation based on traditional methods is mature and novel strategies are emerging. Big data and artificial intelligence combined with forensic science provide new research directions on PMI estimation.

摘要

目的

通过对中国知网(CNKI)收录的死后间隔(PMI)推断研究文献信息进行可视化,探讨1990年1月至2020年8月中国PMI研究的发展历程、不同时期的研究热点、作者及机构间合作情况,为PMI推断研究更好地发展提供参考。

方法

运用信息可视化分析软件CiteSpace 5.7.R1,对CNKI收录的1990年1月至2020年8月PMI推断研究文献中的热点、高频关键词、作者、机构等信息进行大数据分析。

结果

PMI发文高峰时间为2006年至2010年,发文量达114篇。在关键词共现网络中,有效热点词为法医昆虫学、DNA含量分析以及人工智能和大数据等一些新兴词汇。在机构合作网络中,高频机构主要为科研机构。作者合作网络呈现出共聚集和多合作的趋势。

结论

随着科技发展,基于传统方法的PMI估计研究成熟,新策略不断涌现。大数据和人工智能与法医学相结合为PMI估计提供了新的研究方向。

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