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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Vydáno v:Sensors (Basel, Switzerland) Ročník 22; číslo 13; s. 4790
Hlavní autoři: Marti-Puig, Pere, Capra, Chiara, Vega, Daniel, Llunas, Laia, Solé-Casals, Jordi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 24.06.2022
MDPI
Témata:
Accuracy
Anxiety disorders
app
EMA
Emotions
Machine learning
Magnetic resonance imaging
Mental disorders
Mental health
NSSI
Patients
Psychotherapy
Questionnaires
Self destructive behavior
Social anxiety
Statistical methods
Suicides & suicide attempts
Support vector machines
Young adults
ISSN:1424-8220, 1424-8220
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Shrnutí: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.
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These authors contributed equally to this work.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22134790