ArticleJMIR mental health2019
An Approach for Data Mining of Electronic Health Record Data for Suicide Risk Management: Database Analysis for Clinical Decision Support.
Article in JMIR mental health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Application of Natural Language Processing (NLP) in Detecting and Preventing Suicide Ideation: A Systematic Review.International journal of environmental research and public health · 2023Pooled it
- Machine Learning and Natural Language Processing in Mental Health: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Machine learning applications in risk management: Trends and research agenda.F1000Research · 2025Review
- Tracking and Profiling Repeated Users Over Time in Text-Based Counseling: Longitudinal Observational Study With Hierarchical Clustering.Journal of medical Internet research · 2024Observational
- Suicidal vulnerability in older adults and the elderly: study based on risk variables.BJPsych open · 2022Article
- iHealth: The ethics of artificial intelligence and big data in mental healthcare.Internet interventions · 2022Article
- Artificial intelligence and suicide prevention: a systematic review.European psychiatry : the journal of the Association of European Psychiatrists · 2022Review
- Acceptability and satisfaction withFrontiers in psychiatry · 2022Article
- Clinical evaluation of an interoperable clinical decision-support system for the detection of systemic inflammatory response syndrome in critically ill children.BMC medical informatics and decision making · 2021Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn an electronic health context, combining traditional structured clinical assessment methods and routine electronic health-based data capture may be a reliable method to build a dynamic clinical decision-support system (CDSS) for suicide prevention.
objectiveThe aim of this study was to describe the data mining module of a Web-based CDSS and to identify suicide repetition risk in a sample of suicide attempters.
methodsWe analyzed a database of 2802 suicide attempters. Clustering methods were used to identify groups of similar patients, and regression trees were applied to estimate the number of suicide attempts among these patients.
resultsWe identified 3 groups of patients using clustering methods. In addition, relevant risk factors explaining the number of suicide attempts were highlighted by regression trees.
conclusionsData mining techniques can help to identify different groups of patients at risk of suicide reattempt. The findings of this study can be combined with Web-based and smartphone-based data to improve dynamic decision making for clinicians.
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