Title Global suicide mortality rates (2000–2019): clustering, themes, and causes analyzed through machine learning and bibliographic data /
Authors Pranckevičienė, Erinija ; Kasperiūnienė, Judita
DOI 10.3390/ijerph21091202
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Is Part of International journal of environmental research and public health.. Basel : MDPI. 2024, vol. 21, iss. 9, art. no. 1202, p. [1-32].. ISSN 1661-7827. eISSN 1660-4601
Keywords [eng] age-adjusted suicide mortality rate ; association rule mining ; bibliographic analysis ; keyword clustering ; machine learning ; network analysis ; recurrent neural network ; suicide ; text mining ; word embedding
Abstract [eng] Suicide research is directed at understanding social, economic, and biological causes of suicide thoughts and behaviors. (1) Background: Worldwide, certain countries have high suicide mortality rates (SMRs) compared to others. Age-standardized suicide mortality rates (SMRs) published by the World Health Organization (WHO) plus numerous bibliographic records of the Web of Science (WoS) database provide resources to understand these disparities between countries and regions. (2) Methods: Hierarchical clustering was applied to age-standardized suicide mortality rates per 100,000 population from 2000–2019. Keywords of country-specific suicide-related publications collected from WoS were analyzed by network and association rule mining. Keyword embedding was carried out using a recurrent neural network. (3) Results: Countries with similar SMR trends formed naturally distinct groups of high, medium, and low suicide mortality rates. Major themes in suicide research worldwide are depression, mental disorders, youth suicide, euthanasia, hopelessness, loneliness, unemployment, and drugs. Prominent themes differentiating countries and regions include: alcohol in post-Soviet countries; HIV/AIDS in Sub-Saharan Africa, war veterans and PTSD in the Middle East, students in East Asia, and many others. (4) Conclusion: Countries naturally group into high, medium, and low SMR categories characterized by different keyword-informed themes. The compiled dataset and presented methodology enable enrichment of analytical results by bibliographic data where observed results are difficult to interpret.
Published Basel : MDPI
Type Journal article
Language English
Publication date 2024
CC license CC license description