A computational model for assisting individuals with suicidal ideation based on context histories
Pages 1447 - 1466
Abstract
Roughly 800,000 people die from suicide yearly, with suicide being one of the leading causes of death worldwide. For every death by suicide, there are 20 people attempting suicide. Suicidal ideation is a precursor for suicide, thus requiring intervention. Technologies for detection and daily assisting for people who present suicidal ideation can mitigate the impacts of this disorder. In this sense, this work proposes S-Care, a computational model to assist patients who present suicidal ideation in daily lives. The scientific contributions of this work are: (1) integration with external data sources, namely, Intelligent Personal Assistants, patients’ phones, and social media; (2) daily assistance and tracking over time of patients’ Context Histories; (3) risk categories based on the probabilities of the classification; (4) a risk alert heuristic for suicidal ideation; (5) the S-Care Dataset Simulator for creation of suicidal ideation contexts; and (6) the Onto-SCare, an ontology that organizes the knowledge domain of the data analyzed by the model. S-Care analyzes the patients’ expressed thoughts, as well as the location and contact to who the individuals are talking. Furthermore, the model sends personalized alarms to the carers of the patients based on a Risk Alert Heuristic. Thereby, the carers can analyze the Context Information sent in the alerts to take fast actions to avoid any harm to the patients. The evaluation was carried out through simulated scenarios using the generated data from the S-Care Dataset Simulator tool. The experiments presented an average Accuracy of 84.15% for identifying suicidal ideation cases.
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Published: 27 March 2023
Accepted: 02 March 2023
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