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Digital conversations about suicide among teenagers and adults with epilepsy: A big‐data, machine learning analysis
OBJECTIVE: Digital 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 convers...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7384181/ https://www.ncbi.nlm.nih.gov/pubmed/32383797 http://dx.doi.org/10.1111/epi.16507 |
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author | Falcone, Tatiana Dagar, Anjali Castilla-Puentes, Ruby C. Anand, Amit Brethenoux, Caroline Valleta, Liliana G. Furey, Patrick Timmons-Mitchell, Jane Pestana-Knight, Elia |
author_facet | Falcone, Tatiana Dagar, Anjali Castilla-Puentes, Ruby C. Anand, Amit Brethenoux, Caroline Valleta, Liliana G. Furey, Patrick Timmons-Mitchell, Jane Pestana-Knight, Elia |
author_sort | Falcone, Tatiana |
collection | PubMed |
description | OBJECTIVE: Digital 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. METHODS: Advanced 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. RESULTS: A 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. |
format | Online Article Text |
id | pubmed-7384181 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-73841812020-07-28 Digital conversations about suicide among teenagers and adults with epilepsy: A big‐data, machine learning analysis Falcone, Tatiana Dagar, Anjali Castilla-Puentes, Ruby C. Anand, Amit Brethenoux, Caroline Valleta, Liliana G. Furey, Patrick Timmons-Mitchell, Jane Pestana-Knight, Elia Epilepsia Full‐length Original Research OBJECTIVE: Digital 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. METHODS: Advanced 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. RESULTS: A 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. John Wiley and Sons Inc. 2020-05-08 2020-05 /pmc/articles/PMC7384181/ /pubmed/32383797 http://dx.doi.org/10.1111/epi.16507 Text en © 2020 Cleveland Clinic. Epilepsia published by Wiley Periodicals, Inc. on behalf of International League Against Epilepsy This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Full‐length Original Research Falcone, Tatiana Dagar, Anjali Castilla-Puentes, Ruby C. Anand, Amit Brethenoux, Caroline Valleta, Liliana G. Furey, Patrick Timmons-Mitchell, Jane Pestana-Knight, Elia Digital conversations about suicide among teenagers and adults with epilepsy: A big‐data, machine learning analysis |
title | Digital conversations about suicide among teenagers and adults with epilepsy: A big‐data, machine learning analysis |
title_full | Digital conversations about suicide among teenagers and adults with epilepsy: A big‐data, machine learning analysis |
title_fullStr | Digital conversations about suicide among teenagers and adults with epilepsy: A big‐data, machine learning analysis |
title_full_unstemmed | Digital conversations about suicide among teenagers and adults with epilepsy: A big‐data, machine learning analysis |
title_short | Digital conversations about suicide among teenagers and adults with epilepsy: A big‐data, machine learning analysis |
title_sort | digital conversations about suicide among teenagers and adults with epilepsy: a big‐data, machine learning analysis |
topic | Full‐length Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7384181/ https://www.ncbi.nlm.nih.gov/pubmed/32383797 http://dx.doi.org/10.1111/epi.16507 |
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