2022
DOI: 10.3390/s22134790
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A Machine Learning Approach for Predicting Non-Suicidal Self-Injury in Young Adults

Abstract: 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, posi… Show more

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Cited by 8 publications
(5 citation statements)
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“…First, the prediction is performed on the basis of the assessment level. In other work, it could be shown what a difference this can make with respect to the user level 17,22 . Nevertheless, we are currently comparing the user and assessment level with the various options in a larger study.…”
Section: Discussionmentioning
confidence: 99%
“…First, the prediction is performed on the basis of the assessment level. In other work, it could be shown what a difference this can make with respect to the user level 17,22 . Nevertheless, we are currently comparing the user and assessment level with the various options in a larger study.…”
Section: Discussionmentioning
confidence: 99%
“…For instance, within a sample of 1,021 high-risk self-injurious and/or suicidal individuals, Huang et al [76] examined the accuracy of three different complex model types in predicting NSSI across 3, 14, and 28 days. In another study, Marti-Puig et al [77] built a mobile application to collect data so that later they could classify NSSI in young adults focusing on their emotions only. After the data collection phase, they used the data as a time series to test one's involvement in NSSI.…”
Section: Plos Onementioning
confidence: 99%
“…Durch die Implementierung in E-Mental-Health Applikationen, z. B. im Intersession-Monitoring oder in niederschwelligen Interventionen bei subklinischen Bildern, kann die psychotherapeutische Versorgung potentiell ergänzt und verbessert werden [1,11]. Insgesamt -so darf und muss man zusammenfassen -bleibt der erfahrene klinische (menschliche) Blick der Behandelnden aber unabdingbar, wenngleich automatisierte Sprachanalysen zu mehr Evidenzbasierung von individuellen Therapieentscheidungen einer "enhanced Psychotherapy" beitragen können [5].…”
Section: Perspektivenunclassified