SynthesisFrontiers in digital health2022
Structured data vs. unstructured data in machine learning prediction models for suicidal behaviors: A systematic review and meta-analysis.
Synthesis in Frontiers in digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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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
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction models for self-harm and suicide: a systematic review and critical appraisal.BMC medicine · 2025Pooled it
- Predicting Laboratory Test Ordering in Emergency Departments Using Integrated Structured and Unstructured Electronic Health Records: Machine Learning Study.JMIR medical informatics · 2026Article
- Machine Learning Model to Predict Postmastectomy Breast Reconstruction Complications.JAMA network open · 2026Article
- SPEAK-SAFE: secure processing of electronic audio for knowledge in suicide assessment from therapeutic exchanges.Frontiers in digital health · 2026Article
- Multimodal data for predictive medicine: algorithmic fusion of clinical data in anesthesiology and intensive care.Frontiers in medicine · 2026Article
- Changes in Mental State for Help-Seekers of Lifeline Australia's Online Chat Service: Lexical Analysis Approach.JMIR formative research · 2025Article
- An Explainable Artificial Intelligence Text Classifier for Suicidality Prediction in Youth Crisis Text Line Users: Development and Validation Study.JMIR public health and surveillance · 2025Article
- Automatically extracting social determinants of health for suicide: a narrative literature review.Npj mental health research · 2024Review
- Predicting adolescent suicidal behavior following inpatient discharge using structured and unstructured data.Journal of affective disorders · 2024Article
- Explainable artificial intelligence models for predicting risk of suicide using health administrative data in Quebec.PloS one · 2024Article
- Time to Put Down the Phone-A Case for Structured Data Entry.JCO clinical cancer informatics · 2023Article
- Artificial Intelligence in Clinical Diagnosis and Treatment of Dry Eye: Workflows, Effectiveness, and Evaluation.Journal of current ophthalmologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Suicide remains a leading cause of preventable death worldwide, despite advances in research and decreases in mental health stigma through government health campaigns. Machine learning (ML), a type of artificial intelligence (AI), is the use of algorithms to simulate and imitate human cognition. Given the lack of improvement in clinician-based suicide prediction over time, advancements in technology have allowed for novel approaches to predicting suicide risk. This systematic review and meta-analysis aimed to synthesize current research regarding data sources in ML prediction of suicide risk, incorporating and comparing outcomes between structured data (human interpretable such as psychometric instruments) and unstructured data (only machine interpretable such as electronic health records). Online databases and gray literature were searched for studies relating to ML and suicide risk prediction. There were 31 eligible studies. The outcome for all studies combined was AUC = 0.860, structured data showed AUC = 0.873, and unstructured data was calculated at AUC = 0.866. There was substantial heterogeneity between the studies, the sources of which were unable to be defined. The studies showed good accuracy levels in the prediction of suicide risk behavior overall. Structured data and unstructured data also showed similar outcome accuracy according to meta-analysis, despite different volumes and types of input data.
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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.