Innovative Suicide Prevention Methods: The Role of New Technologies and Medical Services in Saving Lives

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Mental Health and Psychosocial Well-being".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 673

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Guest Editor
Department of Experimental and Clinical Pharmacology, Medical University of Warsaw, Banacha 1b Street, 02097 Warsaw, Poland
Interests: public health, healthcare, emergency medicine, disaster medicine

Special Issue Information

Dear Colleagues, 

For many years, there has been significant interest in the issue of suicide worldwide. This is due to rising rates and the alarming forecasts of the World Health Organization (WHO) in this area.

These deaths could likely be avoided because preventing suicidal behavior is possible. For this to be effective, actions taken must be systemic and comprehensive, based on strategies with scientifically proven effectiveness, focused on the individual, group, or entire population.

However, with the development of new technologies, traditional methods of action, especially those affecting the general population, have become significantly personalized. This is an important element of suicide prevention, especially for young people who are proficient in using new technological tools. Mobile apps, personalized applications offering various features such as emotional support or behavioral coaching, virtual reality counseling, the use of artificial intelligence (AI) for data integration and analysis using AI algorithms, as well as machine learning and AI-based algorithms analyzing language patterns, mood, and other signals on social media – these are innovative methods that are becoming part of suicide prevention and risk management strategies. Their further development will open up new possibilities for their practical application, thus contributing to significant improvements in the effectiveness of suicide prevention on a large scale.

The aim of this Special Issue is to publish original research and systematic reviews, both clinical and observational, that analyze the use of innovative methods in suicide prevention in its broadest sense. We invite you to submit manuscripts on the following topics:

  • Applications of new technologies and algorithms based on artificial intelligence in suicide prevention;
  • Implementation and evaluation of therapeutic chatbots based on artificial intelligence used to support mental health;
  • Strategies for the effective implementation of innovative suicide prevention methods;
  • The role of artificial intelligence in the analysis of complex data to predict suicide risk;
  • Evaluation of the effectiveness of implemented innovative solutions in suicide prevention;
  • Prospects for the use of digital tools in the development of suicide prevention methods.

Dr. Dorota Lasota
Guest Editor

Manuscript Submission Information

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Keywords

  • digital tools
  • new technologies
  • medical services
  • suicide
  • suicidal behavior
  • prevention
  • public health
  • innovations

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Published Papers (1 paper)

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Research

32 pages, 4763 KB  
Article
Explainable Text-Based Depression and Suicide Risk Prediction from Social Media Using Deep Learning and Graph Neural Networks
by Atiq Ur Rehman, Abid Iqbal, Ali Sayyed, Zaheer Aslam, Muhammad Ismail Mohmand and Ghassan Husnain
Healthcare 2026, 14(11), 1440; https://doi.org/10.3390/healthcare14111440 - 22 May 2026
Viewed by 323
Abstract
Objectives: The rise in the frequency of mental health concerns (depression and suicide) expressed on social media calls for reliable, explainable, and efficient computational methods for mental health surveillance. In this paper, we propose an interpretable framework for text-based detection of post- and [...] Read more.
Objectives: The rise in the frequency of mental health concerns (depression and suicide) expressed on social media calls for reliable, explainable, and efficient computational methods for mental health surveillance. In this paper, we propose an interpretable framework for text-based detection of post- and community-level mental health risk on social media. Methods: The framework combines (i) Secretary Bird Optimization (SBO) for feature selection of informative linguistic and psychological features, (ii) a BERT (Bidirectional Encoder Representations from Transformers)—CNN (Convolutional Neural Network) model for post-level reasoning, and (iii) a Graph Neural Network (GraphSAGE) for community-level reasoning. The graph is estimated based on semantic similarity between posts and author relations, instead of social interactions (e.g., mentions, replies) between authors. We use SHAP and LIME for model interpretability, uncertainty, and calibration analysis to evaluate the trustworthiness of predictions. Results: The model delivers 93.1% accuracy, 0.91 F1-score, and 0.944 ROC-AUC on the eRisk and CLPsych datasets using a strict user-disjoint validation strategy. SBO lowers the number of features by about 38%, leading to better generalization. The graph-based model enables improved learning of post and user representations by capturing relational dependencies. Conclusions: Our approach offers an explainable and robust means of detecting mental health risk from text. Graph-based representations of semantic and authorship interactions enable community-level analyses, while interpretability and uncertainty estimation facilitate possible human-in-the-loop decision-making. This research does not explicitly consider a human-in-the-loop experiment. Full article
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