ArticleEpilepsia2022
Predictive modeling of suicidal ideation in patients with epilepsy.
Article in Epilepsia, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.
- Pooled it
- Automatically extracting social determinants of health for suicide: a narrative literature review.Npj mental health research · 2024Review
- Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist.Current neurology and neuroscience reports · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveThe prevalence of suicide in the United States has seen an increasing trend and is responsible for 1.6% of all mortality nationwide. Although suicide has the potential to broadly impact the entire population, it has a substantially increased prevalence in persons with epilepsy (PWE), despite many of these individuals consistently seeing a health care provider. The goal of this work is to predict the development of suicidal ideation (SI) in PWE using machine learning methodology such that providers can be better prepared to address suicidality at visits where it is likely to be prominent.
methodsThe current study leverages data collected at an epilepsy clinic during patient visits to predict whether an individual will exhibit SI at their next visit. The data used for prediction consisted of patient responses to questions about the severity of their epilepsy, issues with memory/concentration, somatic problems, markers for mental health, and demographic information. A machine learning approach was then applied to predict whether an individual would display SI at their following visit using only data collected at the prior visit.
resultsThe modeling approach allowed for the successful prediction of an individual's passive and active SI severity at the following visit (r = .42, r = .39) as well as the presence of SI regardless of severity (area under the curve [AUC] = .82, AUC = .8). This shows that the model was successfully able to synthesize the unique combination of an individual's responses to important questions during a clinical visit and utilize that information to indicate whether that individual will exhibit SI at their next visit. SIGNIFICANCE: The results of this modeling approach allow the health care team to be prepared, in advance of a clinical visit, for the potential reporting of SI. By allowing the necessary support to be prepared ahead of time, it can be better integrated at the point of care, where patients are most likely to follow up on potential referrals or treatment.
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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.