Predicting suicidal ideation from irregular and incomplete time series of questionnaires in a smartphone-based suicide prevention platform: a pilot study
Year: 2024
Type: article
Study design: Observational Study
Abstract: Over 700,000 people die by suicide annually. Collecting longitudinal fine-grained data about at-risk individuals, as they occur in the real world, can enhance our understanding of the temporal dynamics of suicide risk, leading to better identification of those in need of immediate intervention. Self-assessment questionnaires were collected over time from 89 at-risk individuals using the EMMA smartphone application. An artificial intelligence (AI) model was trained to assess current level of suicidal ideation (SI), an early indicator of the suicide risk, and to predict its progression in the following days. A key challenge was the unevenly spaced and incomplete nature of the time series data. To address this, the AI was built on a missing value imputation algorithm. The AI successfully distinguished high SI levels from low SI levels both on the current day (AUC = 0.804, F1 = 0.625, MCC = 0.459) and three days in advance (AUC = 0.769, F1 = 0.576, MCC = 0.386). Besides past SI levels, the most significant questions were related to psychological pain, well-being, agitation, emotional tension, and protective factors such as contacts with relatives and leisure activities. This represents a promising step towards early AI-based suicide risk prediction using a smartphone application.
Source: Scientific Reports
Institutions: Inserm, Laboratoire de Traitement de l'Information Médicale, Centre Hospitalier Régional Universitaire de Brest, Centre National de la Recherche Scientifique, Université Paris Cité, Pitié-Salpêtrière Hospital, Hôpital Lapeyronie, Centre de Recherche Médecine, Sciences, Santé, Santé Mentale, Société, Institut du Cerveau, Institut de Génomique Fonctionnelle, Université Paris-Est Créteil, Centre Hospitalier Universitaire Henri-Mondor, Assistance Publique – Hôpitaux de Paris, Hôpitaux Universitaires Henri-Mondor, Hôpital Albert-Chenevier, Université de Lille, Centre Hospitalier Universitaire de Lille, Sciences Cognitives et Sciences Affectives, Université de Montpellier, Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier
Language: English
Subfield: Clinical Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,590
OpenAlex ID: W4402305087