SciTePress - Affiliation Profile

Research.Publish.Connect.

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Name:
Country:
Subject:

Advanced Search Affiliations Search

If you're looking for an exact phrase use quotation marks on text fields.

Proceedings

Proceedings Search

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Papers

Papers Search

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Authors

Authors Search

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Advanced Search

Affiliation

Data and Signal Processing Research Group, Universitat de Vic-Universitat Central de Catalunya, carrer de la Laura 13, Vic, Spain

Context (please use complete words only)

Field

Sort by

1 Papers

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

Topics: Medical Signal Acquisition, Analysis and Processing; Monitoring and Telemetry

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

Digital Object Identifier: 10.5220/0010978500003123

Proceedings of BIOSIGNALS 2022/BIOSTEC, ISBN: 978-989-758-552-4

Quick 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.