ArticleEpilepsia2020
Digital conversations about suicide among teenagers and adults with epilepsy: A big-data, machine learning analysis.
Article in Epilepsia, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 4 of them syntheses 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.
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Who cites it
19 citing papers in PubMed, 4 syntheses or guidelines pooled it, 37 citations in OpenAlex.
- Insights Derived From Text-Based Digital Media, in Relation to Mental Health and Suicide Prevention, Using Data Analysis and Machine Learning: Systematic Review.JMIR mental health · 2024Pooled it
- Transforming epilepsy research: A systematic review on natural language processing applications.Epilepsia · 2023Pooled it
- Natural language processing in clinical neuroscience and psychiatry: A review.Frontiers in psychiatry · 2022Pooled it
- Psychiatric Comorbidities in Pediatric Epilepsy.Current psychiatry reports · 2020Pooled it
- Overhearing hushed voices: Using unobtrusive methods to uncover the work-related sentiments of people with epilepsy.Dialogues in health · 2025Article
- Digital Detection of Suicidal Ideation: A Scoping Review to Inform Prevention and Psychological Well-Being.Behavioral sciences (Basel, Switzerland) · 2025Review
- Knowledge, attitudes, and practices of epilepsy patients regarding the ketogenic diet therapy: A cross-sectional study.Epilepsia open · 2025Article
- Automatically extracting social determinants of health for suicide: a narrative literature review.Npj mental health research · 2024Review
- Patient perceptions of disease burden and treatment of myasthenia gravis based on sentiment analysis of digital conversations.Scientific reports · 2024Article
- Suicide and Epilepsy.Current neurology and neuroscience reports · 2022Review
- Do all patients in the epilepsy monitoring unit experience the same level of comfort? A quantitative exploratory secondary analysis.Journal of advanced nursing · 2022Article
- Applying the Health Belief Model to Characterize Racial/Ethnic Differences in Digital Conversations Related to Depression Pre- and Mid-COVID-19: Descriptive Analysis.JMIR formative research · 2022Article
- A Critical Review of Text Mining Applications for Suicide Research.Current epidemiology reports · 2022Review
- Effect of cognitive behavioral intervention on anxiety, depression, and quality of life in patients with epilepsy.American journal of translational research · 2022Article
- Digital conversations about depression among Hispanics and non-Hispanics in the US: a big-data, machine learning analysis identifies specific characteristics of depression narratives in Hispanics.Annals of general psychiatry · 2021Article
- Analysis of Population Differences in Digital Conversations About Cancer Clinical Trials: Advanced Data Mining and Extraction Study.JMIR cancer · 2021Article
- Self-injurious and suicidal behavior in young adults, teens, and children with epilepsy: A population-based study.Epilepsia · 2020Article
- Digital conversations about suicide among teenagers and adults with epilepsy: A big-data, machine learning analysis.Epilepsia · 2020Article
- Glimpsing the Impact of COVID19 Lock-Down on People With Epilepsy: A Text Mining Approach.Frontiers in neurology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 1 country.
Funding
Abstract
objectiveDigital media conversations can provide important insight into the concerns and struggles of people with epilepsy (PWE) outside of formal clinical settings and help generate useful information for treatment planning. Our study aimed to explore the big data from open-source digital conversations among PWE with regard to suicidality, specifically comparing teenagers and adults, using machine learning technology.
methodsAdvanced machine-learning empowered methodology was used to mine and structure open-source digital conversations of self-identifying teenagers and adults who endorsed suffering from epilepsy and engaged in conversation about suicide. The search was limited to 12 months and included only conversations originating from US internet protocol (IP) addresses. Natural language processing and text analytics were employed to develop a thematic analysis.
resultsA total of 222 000 unique conversations about epilepsy, including 9000 (4%) related to suicide, were posted during the study period. The suicide-related conversations were posted by 7.8% of teenagers and 3.2% of adults in the study. Several critical differences were noted between teenagers and adults. A higher percentage of teenagers are: fearful of "the unknown" due to seizures (63% vs 12% adults), concerned about social consequences of seizures (30% vs 21%), and seek emotional support (29% vs 19%). In contrast, a significantly higher percentage of adults show a defeatist ("given up") attitude compared to teenagers (42% vs 4%). There were important differences in the author's determined sentiments behind the conversations among teenagers and adults. SIGNIFICANCE: In this first of its kind big data analysis of nearly a quarter-million digital conversations about epilepsy using machine learning, we found that teenagers engage in an online conversation about suicide more often than adults. There are some key differences in the attitudes and concerns, which may have implications for the treatment of younger patients with epilepsy.
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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.