Predicting Suicide Attempts with a Long-Short Term Memory Using Environmental Data

EasyChair Preprint no. 13976

Abstract

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.

Keyphrases: Artificial Intelligence, LSTM, mental health, suicide attempts

@Booklet{EasyChair:13976,
  author = {Pol Capdevila and Dolores Rexachs and Jordi Cahué},
  title = {Predicting Suicide Attempts with a Long-Short Term Memory Using Environmental Data},
  howpublished = {EasyChair Preprint no. 13976},

  year = {EasyChair, 2024}}