ArticleFrontiers in psychiatry2017
Acute Mental Discomfort Associated with Suicide Behavior in a Clinical Sample of Patients with Affective Disorders: Ascertaining Critical Variables Using Artificial Intelligence Tools.
Article in Frontiers in psychiatry, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 4 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Machine learning and the prediction of suicide in psychiatric populations: a systematic review.Translational psychiatry · 2024Pooled it
- Structured data vs. unstructured data in machine learning prediction models for suicidal behaviors: A systematic review and meta-analysis.Frontiers in digital health · 2022Pooled it
- Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations.International journal of environmental research and public health · 2020Pooled it
- Data Mining Algorithms and Techniques in Mental Health: A Systematic Review.Journal of medical systems · 2018Pooled it
- Machine learning based identification of suicidal ideation using non-suicidal predictors in a university mental health clinic.Scientific reports · 2025Article
- Quality of care assessment for non-small cell lung cancer patients: transforming routine care data into a continuous improvement system.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Observational
- Artificial intelligence and social intelligence: preliminary comparison study between AI models and psychologists.Frontiers in psychology · 2024Article
- Computational Scientific Discovery in Psychology.Perspectives on psychological science : a journal of the Association for Psychological Science · 2023Article
- Mental Pain Surrounding Suicidal Behaviour: A Review of What Has Been Described and Clinical Recommendations for Help.Frontiers in psychiatry · 2021Review
- New Methods for Assessing Rapid Changes in Suicide Risk.Frontiers in psychiatry · 2021Review
- Testing Suicide Risk Prediction Algorithms Using Phone Measurements With Patients in Acute Mental Health Settings: Feasibility Study.JMIR mHealth and uHealth · 2020Article
- Recognizing states of psychological vulnerability to suicidal behavior: a Bayesian network of artificial intelligence applied to a clinical sample.BMC psychiatry · 2020Article
- Psychotherapy and Artificial Intelligence: A Proposal for Alignment.Frontiers in psychology · 2019Article
- Smartphones, Sensors, and Machine Learning to Advance Real-Time Prediction and Interventions for Suicide Prevention: a Review of Current Progress and Next Steps.Current psychiatry reports · 2018Review
- Artificial intelligence in psychiatry: A systematic review and meta-analysis of diagnostic and therapeutic efficacy.Digital healthReview
- Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimIn efforts to develop reliable methods to detect the likelihood of impending suicidal behaviors, we have proposed the following.
objectiveTo gain a deeper understanding of the state of suicide risk by determining the combination of variables that distinguishes between groups with and without suicide risk.
methodA study involving 707 patients consulting for mental health issues in three health centers in Greater Santiago, Chile. Using 345 variables, an analysis was carried out with artificial intelligence tools, Cross Industry Standard Process for Data Mining processes, and decision tree techniques. The basic algorithm was top-down, and the most suitable division produced by the tree was selected by using the lowest Gini index as a criterion and by looping it until the condition of belonging to the group with suicidal behavior was fulfilled.
resultsFour trees distinguishing the groups were obtained, of which the elements of one were analyzed in greater detail, since this tree included both clinical and personality variables. This specific tree consists of six nodes without suicide risk and eight nodes with suicide risk (tree decision 01, accuracy 0.674, precision 0.652, recall 0.678, specificity 0.670,
conclusionThis study defines the interactions among a group of variables associated with suicidal ideation and behavior. By using these variables, it may be possible to create a quick and easy-to-use tool. As such, psychotherapeutic interventions could be designed to mitigate the impact of these variables on the emotional state of individuals, thereby reducing eventual risk of suicide. Such interventions may reinforce psychological well-being, feelings of self-worth, and reasons for living, for each individual in certain groups of patients.
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