Evidence map›Paper›PMID 39338085›Full record

ArticleInternational journal of environmental research and public health2024

Global Suicide Mortality Rates (2000-2019): Clustering, Themes, and Causes Analyzed through Machine Learning and Bibliographic Data.

Erinija Pranckeviciene, Judita Kasperiuniene

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in
2citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Erinija PranckevicieneFaculty of Informatics, Vytautas Magnus University, LT-53361 Akademija Kauno r., Lithuania.ORCID 0000-0003-1365-3564
Judita KasperiunieneFaculty of Informatics, Vytautas Magnus University, LT-53361 Akademija Kauno r., Lithuania.ORCID 0000-0001-6851-7637

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Machine LearningSuicideCluster AnalysisGlobal HealthHumansage-adjusted suicide mortality rateassociation rule miningbibliographic analysiskeyword clusteringmachine learningnetwork analysisrecurrent neural networksuicidetext miningword embedding

Identifiers

PMID39338085
PMCPMC11431541

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.