Ecological Momentary Assessment and Machine Learning for Predicting Suicidal Ideation
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This study aims to understand what extent does the analysis of daily data encompassing mood fluctuations and contextual stressful events effectively predict short- and long-term suicidal ideation in sexual and gender minority individuals. 103 individuals aged 18 to 29 years found that using 25-day ecological momentary assessment yielded acceptable prediction performance on 1-, 3-, and 8-month suicidal ideation. The prediction effect of feelings faded over time, while the prediction effect of contextual events remained strong. The findings suggest a promising future for detecting suicide ideation over time through the analysis of data on specific types of mood fluctuations and contextual events.
Date: 21 November 2023, 15:00 (Tuesday, 7th week, Michaelmas 2023)
Speaker: Dr Runsen Chen (Tsinghua University)
Organising department: Department of Psychiatry
Organiser: Dr Andrey Kormilitzin (University of Oxford)
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Host: Dr Andrey Kormilitzin (University of Oxford)
Part of: Artificial Intelligence for Mental Health Seminar Series
Booking required?: Not required
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Audience: Public
Editor: Andrey Kormilitzin