Evidence map›Paper›PMID 32383797›Full record

ArticleEpilepsia2020

Digital conversations about suicide among teenagers and adults with epilepsy: A big-data, machine learning analysis.

Tatiana Falcone, Anjali Dagar, Ruby C Castilla-Puentes, Amit Anand, Caroline Brethenoux, Liliana G Valleta, Patrick Furey, Jane Timmons-Mitchell, Elia Pestana-Knight

Open access · hybridAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 4 pooled it
9.1field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 4 syntheses or guidelines pooled it, 37 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
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  4. Psychiatric Comorbidities in Pediatric Epilepsy.Current psychiatry reports · 2020
    Pooled it
  5. Article
  6. Review
  7. Article
  8. Review
  9. Article
  10. Suicide and Epilepsy.Current neurology and neuroscience reports · 2022
    Review
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 4 institutions in 1 country.

Tatiana FalconeDepartment of Psychiatry/Epilepsy, Cleveland Clinic Lerner College of Medicine, Cleveland, OH, USA.
Anjali DagarDepartment of Psychiatry/Epilepsy, Cleveland Clinic Lerner College of Medicine, Cleveland, OH, USA.ORCID 0000-0001-6686-4141
Ruby C Castilla-PuentesDepartment of Neuroscience, Johnson and Johnson, Philadelphia, PA, USA.ORCID 0000-0002-0597-6155
Amit AnandDepartment of Psychiatry/Epilepsy, Cleveland Clinic Lerner College of Medicine, Cleveland, OH, USA.
Caroline BrethenouxCultureintel™ Inc., New York City, NY, USA.
Liliana G ValletaCultureintel™ Inc., New York City, NY, USA.
Patrick FureyCultureintel™ Inc., New York City, NY, USA.
Jane Timmons-MitchellBegun Center for Violence Prevention Research and Education, Case Western Reserve University, Cleveland, OH, USA.
Elia Pestana-KnightDepartment of Psychiatry/Epilepsy, Cleveland Clinic Lerner College of Medicine, Cleveland, OH, USA.
Cleveland Clinic Lerner College of Medicine · USCity Lore the New York Center for Urban Culture · USCase Western Reserve University · USJohnson & Johnson (United States) · US

Funding

HRSA HHS H98MC26260
6 · The paper itself

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.

Indexed as

Big DataMachine LearningAdolescentAdultAge FactorsEpilepsyFemaleHumansMaleNatural Language ProcessingSocial MediaSocial SupportSuicideYoung Adultbig dataepilepsymachine learningsocial mediasuicideteenagers

Identifiers

PMID32383797
PMCPMC7384181
OpenAlexW3023329094

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.