Predicting Suicide Attempts with a Long-Short Term Memory Using Environmental Data
Capdevila Castells, Pol (Universitat Autònoma de Barcelona)
Rexachs, Dolores (Universitat Autònoma de Barcelona)
Cahué, Jordi (Kyndryl)

Publicación:	Springer Cham, 2025
Descripción:	6 pàg.
Resumen:	Hospitals are presently in need of predictive tools to anticipate emergent situations, particularly those pertaining to mental health crises such as suicide attempts. Despite prior research indicating potential influencing factors, the development of effective solutions remains a formidable challenge. However, recent technological advancements, particularly in artificial intelligence (AI), offer promising avenues for addressing this challenge. Building on these advancements, the present study develops and trains a predictive model utilizing a Long Short-Term Memory (LSTM) neural network. The model is trained using data on suicide attempt admissions and environmental variables, as an influence factor, from a hospital in Catalonia. Results demonstrate the potential of AI to provide valuable insights to hospitals, aiding in the management and optimisation of healthcare resources to effectively address mental health emergencies.
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Lengua:	Anglès
Colección:	Lecture notes in computer science ; 15386
Documento:	Capítol de llibre ; recerca ; Versió sotmesa a revisió
Materia:	S ; AI ; LSTM ; Mental health ; Suicide attempts ; SDG 3 - Good Health and Well-being
Publicado en:	Euro-Par 2024: Parallel Processing Workshops. Euro-Par 2024 International Workshops, Madrid, Spain, August 26-30, 2024, Proceedings, Part II, 2025, p. 373-378, ISBN 978-3-031-90203-1

DOI: 10.1007/978-3-031-90203-1_44

Preprint 8 p, 297.4 KB

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