Authors:
Chiara Capra
1
;
2
;
Pere Marti-Puig
1
;
Daniel Vega Moreno
3
;
Laia Llunas
2
;
Stella Nicolaou
3
;
Carlos Schmidt
3
and
Jordi Solé-Casals
1
Affiliations:
1
Data and Signal Processing Research Group, Universitat de Vic-Universitat Central de Catalunya, carrer de la Laura 13, Vic, Spain
;
2
beHIT, carrer de Mata 1, Barcelona, Spain
;
3
Department of Psychiatry & Mental Health, Hospital d’Igualada, Fundació Sanitària Igualada, Igualada, Spain
Keyword(s):
Machine Learning, Digital Mental Health, Non-suicidal Self-injury, Applied AI.
Abstract:
Machine learning (ML) integrated with technology has been a breakthrough in mental health, bringing clinical improvements both for the patient and for the clinician. Among these, real-time patient symptoms’ tracking through ecological momentary assessment (EMA) data can be a valuable tool to forecast symptomatology at the individual-patient level for specific disorders, among which non suicidal self-injury. We aimed at applying classification trees to predict non-suicidal self-injury (NSSI) with EMA data collected through a mobile app. A database of 40 patients diagnosed with borderline personality disorder (BPD) with NSSI (N=22), and a subclinical group of students with NSSI (N=19) was analysed. EMA data was collected by the Sinjur app. Two classification trees were used as models. For the first tree, training results reported 69,7% of accuracy, whereas test results reported 59,3% of accuracy, 87,5% of sensitivity and 58,78% of specificity. For the second tree, training results repo
rted 67,9% of accuracy, whereas test results reported 65,2% of accuracy, 85% of sensitivity and 64,8% of specificity. We concluded that real-time patient monitoring via a mobile app can be a valuable tool for making technology-based predictions at the individual patient level. This promising data needs to be built upon in future studies and needs major translation in the everyday clinical practice to demonstrate its real-world efficacy and later, to be translated to the enterprise world.
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