ArticlePsychological medicine2023
Using machine learning with intensive longitudinal data to predict depression and suicidal ideation among medical interns over time.
Article in Psychological medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 3 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 3 syntheses or guidelines pooled it, 22 citations in OpenAlex.
- Key Features of Digital Phenotyping for Monitoring Mental Disorders: Systematic Review.Journal of medical Internet research · 2025Pooled it
- The Application of AI to Ecological Momentary Assessment Data in Suicide Research: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches.Sensors (Basel, Switzerland) · 2024Pooled it
- Predicting Next-Day Passive Suicidal Ideation in At-Risk Youth.Suicide & life-threatening behavior · 2026Article
- Digital mood phenotyping predicts short- and long-term suicidal ideation across a two-year timescale.Molecular psychiatry · 2026Article
- A scoping review of the use of artificial intelligence as a psychological assessment tool.Translational psychiatry · 2026Article
- Diagnostic accuracy of machine learning approaches for suicide‑related outcomes: a meta‑analysis.Annals of general psychiatry · 2026Article
- Bidirectional associations between negative relationship events and suicidal ideation: an EMA study of stress exposure and generation.Anxiety, stress, and coping · 2026Article
- Predicting Momentary Suicidal Ideation From Smartphone Screenshots Using Vision-Language Models: Prospective Machine Learning Study.JMIR mental health · 2026Article
- Digital Biomarkers in Depression: Integrating DSM-5-TR Symptomatology and RDoC Psychological Systems.Current psychiatry reports · 2026Review
- Automatically extracting social determinants of health for suicide: a narrative literature review.Npj mental health research · 2024Review
- A systematic review on passive sensing for the prediction of suicidal thoughts and behaviors.Npj mental health research · 2024Article
- Effects of frequent assessments on the severity of suicidal thoughts: an ecological momentary assessment study.Frontiers in public health · 2024Article
- Ecological Momentary Assessment and Machine Learning for Predicting Suicidal Ideation Among Sexual and Gender Minority Individuals.JAMA network open · 2023Article
- Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression.NPJ digital medicine · 2023Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
backgroundUse of intensive longitudinal methods (e.g. ecological momentary assessment, passive sensing) and machine learning (ML) models to predict risk for depression and suicide has increased in recent years. However, these studies often vary considerably in length, ML methods used, and sources of data. The present study examined predictive accuracy for depression and suicidal ideation (SI) as a function of time, comparing different combinations of ML methods and data sources.
methodsParticipants were 2459 first-year training physicians (55.1% female; 52.5% White) who were provided with Fitbit wearable devices and assessed daily for mood. Linear [elastic net regression (ENR)] and non-linear (random forest) ML algorithms were used to predict depression and SI at the first-quarter follow-up assessment, using two sets of variables (daily mood features only, daily mood features + passive-sensing features). To assess accuracy over time, models were estimated iteratively for each of the first 92 days of internship, using data available up to that point in time.
resultsENRs using only the daily mood features generally had the best accuracy for predicting mental health outcomes, and predictive accuracy within 1 standard error of the full 92 day models was attained by weeks 7-8. Depression at 92 days could be predicted accurately (area under the curve >0.70) after only 14 days of data collection.
conclusionsSimpler ML methods may outperform more complex methods until passive-sensing features become better specified. For intensive longitudinal studies, there may be limited predictive value in collecting data for more than 2 months.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.