ArticleJournal of affective disorders2024
Temporal prediction of suicidal ideation in an ecological momentary assessment study with recurrent neural networks.
Article in Journal of affective disorders, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of the use of artificial intelligence in mental health-based diabetes care: Current applications and future directions.Diabetic medicine : a journal of the British Diabetic Association · 2026Pooled it
- Artificial intelligence in mental health care: a systematic review of diagnosis, monitoring, and intervention applications.Psychological medicine · 2025Pooled it
- A machine learning-based ecological momentary intervention for mental health promotion in youth: a micro-randomized trial.Translational psychiatry · 2026Trial
- Using smartphone surveys to predict next-week suicide attempts.Journal of psychopathology and clinical science · 2026Article
- Predicting Next-Day Passive Suicidal Ideation in At-Risk Youth.Suicide & life-threatening behavior · 2026Article
- A scoping review of the use of artificial intelligence as a psychological assessment tool.Translational psychiatry · 2026Article
- Position paper on symbiotic intelligence in healthcare: Can AI help us better understand suicidal behavior and prevent suicide?Frontiers in medicine · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
introductionEcological Momentary Assessment (EMA) holds promise for providing insights into daily life experiences when studying mental health phenomena. However, commonly used mixed-effects linear statistical models do not fully utilize the richness of the ultidimensional time-varying data that EMA yields. Recurrent Neural Networks (RNNs) provide an alternative data analytic method to leverage more information and potentially improve prediction, particularly for non-normally distributed outcomes.
methodsAs part of a broader research study of suicidal thoughts and behavior in people with borderline personality disorder (BPD), eighty-four participants engaged in EMA data collection over one week, answering questions multiple times each day about suicidal ideation (SI), stressful events, coping strategy use, and affect. RNNs and mixed-effects linear regression models (MEMs) were trained and used to predict SI. Root mean squared error (RMSE), mean absolute percent error (MAPE), and a pseudo-R
resultsRNNs had superior accuracy metrics (full model: RMSE = 3.41, MAPE = 42 %, pseudo-R
conclusionIn this EMA study with a moderately sized sample, RNNs were better able to learn and predict daily SI compared with mixed-effects models. RNNs should be considered as an option for EMA analysis.
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Registered trials
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