A Machine Learning Approach for Predicting Non-Suicidal Self-Injury in Young Adults

Artificial intelligence techniques were explored to assess the ability to anticipate self-harming behaviour in the mental health context using a database collected by an app previously designed to record the emotional states and activities of a group of subjects exhibiting self-harm. Specifically, t...

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Autores: Marti-Puig, Pere|||0000-0001-6582-4551, Capra, Chiara, Vega, Daniel|||0000-0002-5621-8987, Llunas, Laia, Solé-Casals, Jordi|||0000-0002-6534-1979
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:260297
Acceso en línea:https://ddd.uab.cat/record/260297
https://dx.doi.org/urn:doi:10.3390/s22134790
Access Level:acceso abierto
Palabra clave:NSSI
EMA
App
Machine learning
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spelling A Machine Learning Approach for Predicting Non-Suicidal Self-Injury in Young AdultsMarti-Puig, Pere|||0000-0001-6582-4551Capra, ChiaraVega, Daniel|||0000-0002-5621-8987Llunas, LaiaSolé-Casals, Jordi|||0000-0002-6534-1979NSSIEMAAppMachine learningArtificial intelligence techniques were explored to assess the ability to anticipate self-harming behaviour in the mental health context using a database collected by an app previously designed to record the emotional states and activities of a group of subjects exhibiting self-harm. Specifically, the Leave-One-Subject-Out technique was used to train classification trees with a maximum of five splits. The results show an accuracy of 84.78%, a sensitivity of 64.64% and a specificity of 85.53%. In addition, positive and negative predictive values were also obtained, with results of 14.48% and 98.47%, respectively. These results are in line with those reported in previous work using a multilevel mixed-effect regression analysis. The combination of apps and AI techniques is a powerful way to improve the tools to accompany and support the care and treatment of patients with this type of behaviour. These studies also guide the improvement of apps on the user side, simplifying and collecting more meaningful data, and on the therapist side, progressing in pathology treatments. Traditional therapy involves observing and reconstructing what had happened before episodes once they have occurred. This new generation of tools will make it possible to monitor the pathology more closely and to act preventively. 22022-01-0120222022-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/260297https://dx.doi.org/urn:doi:10.3390/s22134790reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengAgència de Gestió d'Ajuts Universitaris i de Recerca https://doi.org/10.13039/501100003030 2020-DI-068open accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:2602972026-06-06T12:50:31Z
dc.title.none.fl_str_mv A Machine Learning Approach for Predicting Non-Suicidal Self-Injury in Young Adults
title A Machine Learning Approach for Predicting Non-Suicidal Self-Injury in Young Adults
spellingShingle A Machine Learning Approach for Predicting Non-Suicidal Self-Injury in Young Adults
Marti-Puig, Pere|||0000-0001-6582-4551
NSSI
EMA
App
Machine learning
title_short A Machine Learning Approach for Predicting Non-Suicidal Self-Injury in Young Adults
title_full A Machine Learning Approach for Predicting Non-Suicidal Self-Injury in Young Adults
title_fullStr A Machine Learning Approach for Predicting Non-Suicidal Self-Injury in Young Adults
title_full_unstemmed A Machine Learning Approach for Predicting Non-Suicidal Self-Injury in Young Adults
title_sort A Machine Learning Approach for Predicting Non-Suicidal Self-Injury in Young Adults
dc.creator.none.fl_str_mv Marti-Puig, Pere|||0000-0001-6582-4551
Capra, Chiara
Vega, Daniel|||0000-0002-5621-8987
Llunas, Laia
Solé-Casals, Jordi|||0000-0002-6534-1979
author Marti-Puig, Pere|||0000-0001-6582-4551
author_facet Marti-Puig, Pere|||0000-0001-6582-4551
Capra, Chiara
Vega, Daniel|||0000-0002-5621-8987
Llunas, Laia
Solé-Casals, Jordi|||0000-0002-6534-1979
author_role author
author2 Capra, Chiara
Vega, Daniel|||0000-0002-5621-8987
Llunas, Laia
Solé-Casals, Jordi|||0000-0002-6534-1979
author2_role author
author
author
author
dc.subject.none.fl_str_mv NSSI
EMA
App
Machine learning
topic NSSI
EMA
App
Machine learning
description Artificial intelligence techniques were explored to assess the ability to anticipate self-harming behaviour in the mental health context using a database collected by an app previously designed to record the emotional states and activities of a group of subjects exhibiting self-harm. Specifically, the Leave-One-Subject-Out technique was used to train classification trees with a maximum of five splits. The results show an accuracy of 84.78%, a sensitivity of 64.64% and a specificity of 85.53%. In addition, positive and negative predictive values were also obtained, with results of 14.48% and 98.47%, respectively. These results are in line with those reported in previous work using a multilevel mixed-effect regression analysis. The combination of apps and AI techniques is a powerful way to improve the tools to accompany and support the care and treatment of patients with this type of behaviour. These studies also guide the improvement of apps on the user side, simplifying and collecting more meaningful data, and on the therapist side, progressing in pathology treatments. Traditional therapy involves observing and reconstructing what had happened before episodes once they have occurred. This new generation of tools will make it possible to monitor the pathology more closely and to act preventively.
publishDate 2022
dc.date.none.fl_str_mv 2
2022-01-01
2022
2022-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/260297
https://dx.doi.org/urn:doi:10.3390/s22134790
url https://ddd.uab.cat/record/260297
https://dx.doi.org/urn:doi:10.3390/s22134790
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agència de Gestió d'Ajuts Universitaris i de Recerca https://doi.org/10.13039/501100003030 2020-DI-068
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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