A Machine Learning Approach for Predicting Non-Suicidal Self-Injury in Young Adults
Marti-Puig, Pere (Universitat de Vic)
Capra, Chiara (Universitat de Vic)
Vega Moreno, Daniel (Universitat Autònoma de Barcelona. Departament de Psicologia Clínica i de la Salut)
Llunas, Laia (beHIT)
Solé-Casals, Jordi (Universitat de Vic)

Data:	2022
Resum:	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.
Ajuts:	Agència de Gestió d'Ajuts Universitaris i de Recerca 2020-DI-068
Drets:	Aquest 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.
Llengua:	Anglès
Document:	Article ; Versió publicada
Matèria:	NSSI ; EMA ; App ; Machine learning
Publicat a:	Sensors (Basel, Switzerland), Vol. 22 Núm. 13 (2022) , p. 4790, ISSN 1424-8220

DOI: 10.3390/s22134790
PMID: 35808286

14 p, 1.7 MB

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