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Suicide Risk Analysis and Psycho-Emotional Risk Factors Using an Artificial Neural Network System
Suicidal behavior among young people has become an increasingly relevant topic after the COVID-19 pandemic and constitutes a public health problem. This study aimed to examine the variables associated with suicide risk and determine their predictive capacity. The specific objectives were: (1) to ana...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10454187/ https://www.ncbi.nlm.nih.gov/pubmed/37628534 http://dx.doi.org/10.3390/healthcare11162337 |
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author | Morales-Rodríguez, Francisco Manuel Martínez-Ramón, Juan Pedro Giménez-Lozano, José Miguel Morales Rodríguez, Ana María |
author_facet | Morales-Rodríguez, Francisco Manuel Martínez-Ramón, Juan Pedro Giménez-Lozano, José Miguel Morales Rodríguez, Ana María |
author_sort | Morales-Rodríguez, Francisco Manuel |
collection | PubMed |
description | Suicidal behavior among young people has become an increasingly relevant topic after the COVID-19 pandemic and constitutes a public health problem. This study aimed to examine the variables associated with suicide risk and determine their predictive capacity. The specific objectives were: (1) to analyze the relationship between suicide risk and model variables and (2) to design an artificial neural network (ANN) with predictive capacity for suicide risk. The sample comprised 337 youths aged 18–33 years. An ex post facto design was used. The results showed that emotional attention, followed by problem solving and perfectionism, were variables that contributed the most to the ANN’s predictive capacity. The ANN achieved a hit rate of 85.7%, which is much higher than chance, and with only 14.3% of incorrect cases. This study extracted relevant information on suicide risk and the related risk and protective factors via artificial intelligence. These data will be useful for diagnosis as well as for psycho-educational guidance and prevention. This study was one of the first to apply this innovative methodology based on an ANN design to study these variables. |
format | Online Article Text |
id | pubmed-10454187 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104541872023-08-26 Suicide Risk Analysis and Psycho-Emotional Risk Factors Using an Artificial Neural Network System Morales-Rodríguez, Francisco Manuel Martínez-Ramón, Juan Pedro Giménez-Lozano, José Miguel Morales Rodríguez, Ana María Healthcare (Basel) Article Suicidal behavior among young people has become an increasingly relevant topic after the COVID-19 pandemic and constitutes a public health problem. This study aimed to examine the variables associated with suicide risk and determine their predictive capacity. The specific objectives were: (1) to analyze the relationship between suicide risk and model variables and (2) to design an artificial neural network (ANN) with predictive capacity for suicide risk. The sample comprised 337 youths aged 18–33 years. An ex post facto design was used. The results showed that emotional attention, followed by problem solving and perfectionism, were variables that contributed the most to the ANN’s predictive capacity. The ANN achieved a hit rate of 85.7%, which is much higher than chance, and with only 14.3% of incorrect cases. This study extracted relevant information on suicide risk and the related risk and protective factors via artificial intelligence. These data will be useful for diagnosis as well as for psycho-educational guidance and prevention. This study was one of the first to apply this innovative methodology based on an ANN design to study these variables. MDPI 2023-08-18 /pmc/articles/PMC10454187/ /pubmed/37628534 http://dx.doi.org/10.3390/healthcare11162337 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Morales-Rodríguez, Francisco Manuel Martínez-Ramón, Juan Pedro Giménez-Lozano, José Miguel Morales Rodríguez, Ana María Suicide Risk Analysis and Psycho-Emotional Risk Factors Using an Artificial Neural Network System |
title | Suicide Risk Analysis and Psycho-Emotional Risk Factors Using an Artificial Neural Network System |
title_full | Suicide Risk Analysis and Psycho-Emotional Risk Factors Using an Artificial Neural Network System |
title_fullStr | Suicide Risk Analysis and Psycho-Emotional Risk Factors Using an Artificial Neural Network System |
title_full_unstemmed | Suicide Risk Analysis and Psycho-Emotional Risk Factors Using an Artificial Neural Network System |
title_short | Suicide Risk Analysis and Psycho-Emotional Risk Factors Using an Artificial Neural Network System |
title_sort | suicide risk analysis and psycho-emotional risk factors using an artificial neural network system |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10454187/ https://www.ncbi.nlm.nih.gov/pubmed/37628534 http://dx.doi.org/10.3390/healthcare11162337 |
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