Evidence map›Paper›PMID 39637380›Full record

ArticleJournal of medical Internet research2024

Machine Learning-Based Suicide Risk Prediction Model for Suicidal Trajectory on Social Media Following Suicidal Mentions: Independent Algorithm Validation.

Zachary Kaminsky, Robyn J McQuaid, Kim Gc Hellemans, Zachary R Patterson, Mysa Saad, Robert L Gabrys, Tetyana Kendzerska, Alfonso Abizaid, Rebecca Robillard

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Indirect, machine learning-based suicide risk screening: Evidence from cross-National Validation.European psychiatry : the journal of the Association of European Psychiatrists · 2026
    Article
  4. Article
  5. Review
  6. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Zachary KaminskyUniversity of Ottawa Institute of Mental Health Research at The Royal, Ottawa, ON, Canada.ORCID 0000-0001-7917-1301
Robyn J McQuaidUniversity of Ottawa Institute of Mental Health Research at The Royal, Ottawa, ON, Canada.ORCID 0000-0003-4242-3135
Kim Gc HellemansDepartment of Neuroscience, Carleton University, Ottawa, ON, Canada.ORCID 0000-0001-9169-7314
Zachary R PattersonDepartment of Neuroscience, Carleton University, Ottawa, ON, Canada.ORCID 0000-0001-7586-563X
Mysa SaadUniversity of Ottawa Institute of Mental Health Research at The Royal, Ottawa, ON, Canada.ORCID 0000-0003-0611-559X
Robert L GabrysDepartment of Neuroscience, Carleton University, Ottawa, ON, Canada.ORCID 0000-0002-9644-257X
Tetyana KendzerskaFaculty of Medicine, University of Ottawa, Ottawa, ON, Canada.ORCID 0000-0002-5301-1796
Alfonso AbizaidDepartment of Neuroscience, Carleton University, Ottawa, ON, Canada.ORCID 0000-0001-9303-3511
Rebecca RobillardUniversity of Ottawa Institute of Mental Health Research at The Royal, Ottawa, ON, Canada.ORCID 0000-0002-1491-997X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrevious efforts to apply machine learning-based natural language processing to longitudinally collected social media data have shown promise in predicting suicide risk.

objectiveOur primary objective was to externally validate our previous machine learning algorithm, the Suicide Artificial Intelligence Prediction Heuristic (SAIPH), against external survey data in 2 independent cohorts. A second objective was to evaluate the efficacy of SAIPH as an indicator of changing suicidal ideation (SI) over time. The tertiary objective was to use SAIPH to evaluate factors important for improving or worsening suicidal trajectory on social media following suicidal mention.

methodsTwitter (subsequently rebranded as X) timeline data from a student survey cohort and COVID-19 survey cohort were scored using SAIPH and compared to SI questions on the Beck Depression Inventory and the Self-Report version of the Quick Inventory of Depressive Symptomatology in 159 and 307 individuals, respectively. SAIPH was used to evaluate changing SI trajectory following suicidal mentions in 2 cohorts collected using the Twitter application programming interface.

resultsAn interaction of the mean SAIPH score derived from 12 days of Twitter data before survey completion and the average number of posts per day was associated with quantitative SI metrics in each cohort (student survey cohort interaction β=.038, SD 0.014; F

conclusionsTaken together, our results not only validate the association of SAIPH with perceived stress, SI, and changing SI over time but also generate novel methods to evaluate the effects of social media interactions on changing suicidal trajectory.

Indexed as

COVID-19Machine LearningSocial MediaSuicidal IdeationAdultAlgorithmsCohort StudiesFemaleHumansMaleNatural Language ProcessingRisk AssessmentSuicideSurveys and QuestionnairesYoung Adultmachine learningnatural language processingpredictionsocial mediasuicidal ideationsuicidal mentionsuicidesuicide risksuicide risk modelTwittervalidation

Identifiers

PMID39637380
PMCPMC11659700

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.