ArticleNpj mental health research2024
Dynamic learning of individual-level suicidal ideation trajectories to enhance mental health care.
Article in Npj mental health research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
5 citing papers in PubMed.
- Rethinking AI in youth mental health: promise, perils, and ethical integration.International journal of mental health systems · 2026Article
- Using Digital Mental Health and Measurement-Based Care With Youth Experiencing Suicidal Thoughts and Behaviors: Qualitative Study of Clinician Barriers and Facilitators.JMIR human factors · 2026Article
- Personalized and collaborative use of digital measurement-based care tools enhances engagement among young adults: a mixed-methods study.BMC health services research · 2025Article
- A prognostic model for predicting functional impairment in youth mental health services.European psychiatry : the journal of the Association of European Psychiatrists · 2024Article
- A Digital Approach for Addressing Suicidal Ideation and Behaviors in Youth Mental Health Services: Observational Study.Journal of medical Internet research · 2024Observational
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
There has recently been an increase in ongoing patient-report routine outcome monitoring for individuals within clinical care, which has corresponded to increased longitudinal information about an individual. However, many models that are aimed at clinical practice have difficulty fully incorporating this information. This is in part due to the difficulty in dealing with the irregularly time-spaced observations that are common in clinical data. Consequently, we built individual-level continuous-time trajectory models of suicidal ideation for a clinical population (N = 585) with data collected via a digital platform. We demonstrate how such models predict an individual's level and variability of future suicide ideation, with implications for the frequency that individuals may need to be observed. These individual-level predictions provide a more personalised understanding than other predictive methods and have implications for enhanced measurement-based care.
Identifiers
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Registered trials
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.