Abraham Kochumol, K R Anish, Toms Greety, Francis P Nice Mary, Babu Jobi
Department of Computer Applications, Marian College Kuttikkanam, Peermade, IND.
Department of Social Work, Rajagiri College of Social Sciences, Kalamassery, IND.
Cureus. 2024 Jun 11;16(6):e62139. doi: 10.7759/cureus.62139. eCollection 2024 Jun.
Suicide remains a critical global health issue despite advancements in mental health treatment. The purpose of this analysis is to emphasize the development, patterns, and noteworthy outcomes of suicide prediction research. It also helps to uncover gaps and areas of under-researched topics within suicide prediction. A scientometric analysis was conducted using Biblioshiny and VOSviewer. To thoroughly assess the academic literature on suicide prediction, various scientometric methodologies such as trend analysis and citation analysis were employed. We utilized the temporal features of the Web of Science to analyze publication trends over time. Author affiliation data were used to investigate the geographic distribution of research. Cluster analysis was performed by grouping related keywords into clusters to identify overarching themes within the literature. A total of 1,703 articles from 828 different sources, spanning from 1942 to 2023, were collected for the analysis. Machine learning techniques might have a big influence on suicide-related event prediction, which would enhance attempts at suicide prevention and intervention. The conceptual understanding of suicide prediction is enhanced by scientometric analysis, which further uncovers the research gap and literature in this area. Suicide prediction research underscores that suicidal behavior is not caused by a single factor but is the result of a complex interplay of multiple factors. These factors may include biological, psychological, social, and environmental factors. Understanding and integrating these factors into predictive models is a theoretical advancement in the field. Unlike previous bibliometric studies in the field of suicide prediction that have typically focused on specific subtopics or data sources, our analysis offers a comprehensive mapping of the entire landscape. We encompass a wide range of suicide prediction literature, including research from medical, psychological, and social science domains, thus providing a holistic overview.
尽管心理健康治疗取得了进展，但自杀仍然是一个关键的全球健康问题。本分析的目的是强调自杀预测研究的发展、模式和值得关注的成果。它还有助于发现自杀预测领域研究不足的差距和领域。使用Biblioshiny和VOSviewer进行了科学计量分析。为了全面评估关于自杀预测的学术文献，采用了各种科学计量方法，如趋势分析和引文分析。我们利用科学引文索引（Web of Science）的时间特征来分析随时间的出版趋势。作者所属机构数据用于调查研究的地理分布。通过将相关关键词分组进行聚类分析，以识别文献中的总体主题。总共收集了1942年至2023年期间来自828个不同来源的1703篇文章进行分析。机器学习技术可能对自杀相关事件预测有重大影响，这将加强自杀预防和干预的努力。科学计量分析增强了对自杀预测的概念理解，进一步揭示了该领域的研究差距和文献。自杀预测研究强调，自杀行为不是由单一因素引起的，而是多种因素复杂相互作用的结果。这些因素可能包括生物、心理、社会和环境因素。将这些因素理解并整合到预测模型中是该领域的一项理论进步。与以往自杀预测领域的文献计量研究通常只关注特定子主题或数据源不同，我们的分析提供了整个领域的全面映射。我们涵盖了广泛的自杀预测文献，包括医学、心理学和社会科学领域的研究，从而提供了一个全面的概述。
