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Authors: Chiara Capra 1 ; 2 ; Pere Marti-Puig 2 ; Daniel Moreno 3 ; Laia Llunas 1 ; Stella Nicolaou 3 ; Carlos Schmidt 3 and Jordi Solé-Casals 2

Affiliations: 1 beHIT, carrer de Mata 1, Barcelona, Spain ; 2 Data and Signal Processing Research Group, Universitat de Vic-Universitat Central de Catalunya, carrer de la Laura 13, Vic, 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. (More)

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Paper citation in several formats:
Capra, C.; Marti-Puig, P.; Moreno, D.; Llunas, L.; Nicolaou, S.; Schmidt, C. and Solé-Casals, J. (2022). Preliminary Results on the Use of Classification Trees to Predict Non-suicidal Self-injury with Data Collected through a Mobile App. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSIGNALS, ISBN 978-989-758-552-4; ISSN 2184-4305, pages 278-282. DOI: 10.5220/0010978500003123

@conference{biosignals22,
author={Chiara Capra. and Pere Marti{-}Puig. and Daniel Moreno. and Laia Llunas. and Stella Nicolaou. and Carlos Schmidt. and Jordi Solé{-}Casals.},
title={Preliminary Results on the Use of Classification Trees to Predict Non-suicidal Self-injury with Data Collected through a Mobile App},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSIGNALS,},
year={2022},
pages={278-282},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010978500003123},
isbn={978-989-758-552-4},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSIGNALS,
TI - Preliminary Results on the Use of Classification Trees to Predict Non-suicidal Self-injury with Data Collected through a Mobile App
SN - 978-989-758-552-4
IS - 2184-4305
AU - Capra, C.
AU - Marti-Puig, P.
AU - Moreno, D.
AU - Llunas, L.
AU - Nicolaou, S.
AU - Schmidt, C.
AU - Solé-Casals, J.
PY - 2022
SP - 278
EP - 282
DO - 10.5220/0010978500003123