Preliminary Results on the Use of Classification Trees to Predict Non-suicidal Self-injury with Data Collected through a Mobile App

Chiara Capra, Chiara Capra, Pere Marti-Puig, Daniel Vega Moreno, Laia Llunas, Stella Nicolaou, Carlos Schmidt, Jordi Solé-Casals

2022

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 reported 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.

Paper Citation

in Harvard Style

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 (BIOSTEC 2022) - Volume 4: BIOSIGNALS; ISBN 978-989-758-552-4, SciTePress, pages 278-282. DOI: 10.5220/0010978500003123

in Bibtex Style

@conference{biosignals22,
author={Chiara Capra and Pere Marti-Puig and Daniel Vega 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 (BIOSTEC 2022) - Volume 4: BIOSIGNALS},
year={2022},
pages={278-282},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010978500003123},
isbn={978-989-758-552-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 4: 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
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
PB - SciTePress