Cargando…

Trends in HIV Terminology: Text Mining and Data Visualization Assessment of International AIDS Conference Abstracts Over 25 Years

BACKGROUND: The language encompassing health conditions can also influence behaviors that affect health outcomes. Few published quantitative studies have been conducted that evaluate HIV-related terminology changes over time. To expand this research, this study included an analysis of a dataset of a...

Descripción completa

Detalles Bibliográficos
Autores principales: Dancy-Scott, Nicole, Dutcher, Gale A, Keselman, Alla, Hochstein, Colette, Copty, Christina, Ben-Senia, Diane, Rajan, Sampada, Asencio, Maria Guadalupe, Choi, Jason Jongwon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5960041/
https://www.ncbi.nlm.nih.gov/pubmed/29728344
http://dx.doi.org/10.2196/publichealth.8552
_version_ 1783324510663999488
author Dancy-Scott, Nicole
Dutcher, Gale A
Keselman, Alla
Hochstein, Colette
Copty, Christina
Ben-Senia, Diane
Rajan, Sampada
Asencio, Maria Guadalupe
Choi, Jason Jongwon
author_facet Dancy-Scott, Nicole
Dutcher, Gale A
Keselman, Alla
Hochstein, Colette
Copty, Christina
Ben-Senia, Diane
Rajan, Sampada
Asencio, Maria Guadalupe
Choi, Jason Jongwon
author_sort Dancy-Scott, Nicole
collection PubMed
description BACKGROUND: The language encompassing health conditions can also influence behaviors that affect health outcomes. Few published quantitative studies have been conducted that evaluate HIV-related terminology changes over time. To expand this research, this study included an analysis of a dataset of abstracts presented at the International AIDS Conference (IAC) from 1989 to 2014. These abstracts reflect the global response to HIV over 25 years. Two powerful methodologies were used to evaluate the dataset: text mining to convert the unstructured information into structured data for analysis and data visualization to represent the data visually to assess trends. OBJECTIVE: The purpose of this project was to evaluate the evolving use of HIV-related language in abstracts presented at the IAC from 1989 to 2014. METHODS: Over 80,000 abstracts were obtained from the International AIDS Society and imported into a Microsoft SQL Server database for data processing and text mining analyses. A text mining module within the KNIME Analytics Platform, an open source software, was then used to mine the partially processed data to create a terminology corpus of key HIV terms. Subject matter experts grouped the terms into categories. Tableau, a data visualization software, was used to visualize the frequency metrics associated with the terms as line graphs and word clouds. The visualized dashboards were reviewed to discern changes in terminology use across IAC years. RESULTS: The major findings identify trends in HIV-related terminology over 25 years. The term “AIDS epidemic” was dominantly used from 1989 to 1991 and then declined in use. In contrast, use of the term “HIV epidemic” increased through 2014. Beginning in the mid-1990s, the term “treatment experienced” appeared with increasing frequency in the abstracts. Use of terms identifying individuals as “carriers or victims” of HIV rarely appeared after 2008. Use of the terms “HIV positive” and “HIV infected” peaked in the early-1990s and then declined in use. The terms “men who have sex with men” and “MSM” were rarely used until 1994; subsequently, use of these terms increased through 2014. The term “sex worker” steadily increased in frequency throughout conference years, whereas the term “prostitute” decreased over time. CONCLUSIONS: The results of this study highlight changes in HIV terminology use over 25 years, including the addition, disappearance, and changing use of terms that reflect advances in HIV research and medical practice and destigmatization of the disease. Coupled with findings from related quantitative research, HIV-related terminology recommendations based on results of this study are included. Adoption of these recommendations will further efforts to use less stigmatizing language and facilitate effective communication between health professionals and people affected by HIV.
format Online
Article
Text
id pubmed-5960041
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher JMIR Publications
record_format MEDLINE/PubMed
spelling pubmed-59600412018-05-30 Trends in HIV Terminology: Text Mining and Data Visualization Assessment of International AIDS Conference Abstracts Over 25 Years Dancy-Scott, Nicole Dutcher, Gale A Keselman, Alla Hochstein, Colette Copty, Christina Ben-Senia, Diane Rajan, Sampada Asencio, Maria Guadalupe Choi, Jason Jongwon JMIR Public Health Surveill Original Paper BACKGROUND: The language encompassing health conditions can also influence behaviors that affect health outcomes. Few published quantitative studies have been conducted that evaluate HIV-related terminology changes over time. To expand this research, this study included an analysis of a dataset of abstracts presented at the International AIDS Conference (IAC) from 1989 to 2014. These abstracts reflect the global response to HIV over 25 years. Two powerful methodologies were used to evaluate the dataset: text mining to convert the unstructured information into structured data for analysis and data visualization to represent the data visually to assess trends. OBJECTIVE: The purpose of this project was to evaluate the evolving use of HIV-related language in abstracts presented at the IAC from 1989 to 2014. METHODS: Over 80,000 abstracts were obtained from the International AIDS Society and imported into a Microsoft SQL Server database for data processing and text mining analyses. A text mining module within the KNIME Analytics Platform, an open source software, was then used to mine the partially processed data to create a terminology corpus of key HIV terms. Subject matter experts grouped the terms into categories. Tableau, a data visualization software, was used to visualize the frequency metrics associated with the terms as line graphs and word clouds. The visualized dashboards were reviewed to discern changes in terminology use across IAC years. RESULTS: The major findings identify trends in HIV-related terminology over 25 years. The term “AIDS epidemic” was dominantly used from 1989 to 1991 and then declined in use. In contrast, use of the term “HIV epidemic” increased through 2014. Beginning in the mid-1990s, the term “treatment experienced” appeared with increasing frequency in the abstracts. Use of terms identifying individuals as “carriers or victims” of HIV rarely appeared after 2008. Use of the terms “HIV positive” and “HIV infected” peaked in the early-1990s and then declined in use. The terms “men who have sex with men” and “MSM” were rarely used until 1994; subsequently, use of these terms increased through 2014. The term “sex worker” steadily increased in frequency throughout conference years, whereas the term “prostitute” decreased over time. CONCLUSIONS: The results of this study highlight changes in HIV terminology use over 25 years, including the addition, disappearance, and changing use of terms that reflect advances in HIV research and medical practice and destigmatization of the disease. Coupled with findings from related quantitative research, HIV-related terminology recommendations based on results of this study are included. Adoption of these recommendations will further efforts to use less stigmatizing language and facilitate effective communication between health professionals and people affected by HIV. JMIR Publications 2018-05-04 /pmc/articles/PMC5960041/ /pubmed/29728344 http://dx.doi.org/10.2196/publichealth.8552 Text en ©Nicole Dancy-Scott, Gale A Dutcher, Alla Keselman, Colette Hochstein, Christina Copty, Diane Ben-Senia, Sampada Rajan, Maria Guadalupe Asencio, Jason Jongwon Choi. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 04.05.2018. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on http://publichealth.jmir.org, as well as this copyright and license information must be included.
spellingShingle Original Paper
Dancy-Scott, Nicole
Dutcher, Gale A
Keselman, Alla
Hochstein, Colette
Copty, Christina
Ben-Senia, Diane
Rajan, Sampada
Asencio, Maria Guadalupe
Choi, Jason Jongwon
Trends in HIV Terminology: Text Mining and Data Visualization Assessment of International AIDS Conference Abstracts Over 25 Years
title Trends in HIV Terminology: Text Mining and Data Visualization Assessment of International AIDS Conference Abstracts Over 25 Years
title_full Trends in HIV Terminology: Text Mining and Data Visualization Assessment of International AIDS Conference Abstracts Over 25 Years
title_fullStr Trends in HIV Terminology: Text Mining and Data Visualization Assessment of International AIDS Conference Abstracts Over 25 Years
title_full_unstemmed Trends in HIV Terminology: Text Mining and Data Visualization Assessment of International AIDS Conference Abstracts Over 25 Years
title_short Trends in HIV Terminology: Text Mining and Data Visualization Assessment of International AIDS Conference Abstracts Over 25 Years
title_sort trends in hiv terminology: text mining and data visualization assessment of international aids conference abstracts over 25 years
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5960041/
https://www.ncbi.nlm.nih.gov/pubmed/29728344
http://dx.doi.org/10.2196/publichealth.8552
work_keys_str_mv AT dancyscottnicole trendsinhivterminologytextmininganddatavisualizationassessmentofinternationalaidsconferenceabstractsover25years
AT dutchergalea trendsinhivterminologytextmininganddatavisualizationassessmentofinternationalaidsconferenceabstractsover25years
AT keselmanalla trendsinhivterminologytextmininganddatavisualizationassessmentofinternationalaidsconferenceabstractsover25years
AT hochsteincolette trendsinhivterminologytextmininganddatavisualizationassessmentofinternationalaidsconferenceabstractsover25years
AT coptychristina trendsinhivterminologytextmininganddatavisualizationassessmentofinternationalaidsconferenceabstractsover25years
AT benseniadiane trendsinhivterminologytextmininganddatavisualizationassessmentofinternationalaidsconferenceabstractsover25years
AT rajansampada trendsinhivterminologytextmininganddatavisualizationassessmentofinternationalaidsconferenceabstractsover25years
AT asenciomariaguadalupe trendsinhivterminologytextmininganddatavisualizationassessmentofinternationalaidsconferenceabstractsover25years
AT choijasonjongwon trendsinhivterminologytextmininganddatavisualizationassessmentofinternationalaidsconferenceabstractsover25years