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Assessing Unmet Information Needs of Breast Cancer Survivors: Exploratory Study of Online Health Forums Using Text Classification and Retrieval

BACKGROUND: Patient education materials given to breast cancer survivors may not be a good fit for their information needs. Needs may change over time, be forgotten, or be misreported, for a variety of reasons. An automated content analysis of survivors' postings to online health forums can ide...

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Detalles Bibliográficos
Autores principales: McRoy, Susan, Rastegar-Mojarad, Majid, Wang, Yanshan, Ruddy, Kathryn J, Haddad, Tufia C, Liu, Hongfang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5974460/
https://www.ncbi.nlm.nih.gov/pubmed/29764801
http://dx.doi.org/10.2196/cancer.9050
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author McRoy, Susan
Rastegar-Mojarad, Majid
Wang, Yanshan
Ruddy, Kathryn J
Haddad, Tufia C
Liu, Hongfang
author_facet McRoy, Susan
Rastegar-Mojarad, Majid
Wang, Yanshan
Ruddy, Kathryn J
Haddad, Tufia C
Liu, Hongfang
author_sort McRoy, Susan
collection PubMed
description BACKGROUND: Patient education materials given to breast cancer survivors may not be a good fit for their information needs. Needs may change over time, be forgotten, or be misreported, for a variety of reasons. An automated content analysis of survivors' postings to online health forums can identify expressed information needs over a span of time and be repeated regularly at low cost. Identifying these unmet needs can guide improvements to existing education materials and the creation of new resources. OBJECTIVE: The primary goals of this project are to assess the unmet information needs of breast cancer survivors from their own perspectives and to identify gaps between information needs and current education materials. METHODS: This approach employs computational methods for content modeling and supervised text classification to data from online health forums to identify explicit and implicit requests for health-related information. Potential gaps between needs and education materials are identified using techniques from information retrieval. RESULTS: We provide a new taxonomy for the classification of sentences in online health forum data. 260 postings from two online health forums were selected, yielding 4179 sentences for coding. After annotation of data and training alternative one-versus-others classifiers, a random forest-based approach achieved F1 scores from 66% (Other, dataset2) to 90% (Medical, dataset1) on the primary information types. 136 expressions of need were used to generate queries to indexed education materials. Upon examination of the best two pages retrieved for each query, 12% (17/136) of queries were found to have relevant content by all coders, and 33% (45/136) were judged to have relevant content by at least one. CONCLUSIONS: Text from online health forums can be analyzed effectively using automated methods. Our analysis confirms that breast cancer survivors have many information needs that are not covered by the written documents they typically receive, as our results suggest that at most a third of breast cancer survivors’ questions would be addressed by the materials currently provided to them.
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spelling pubmed-59744602018-06-01 Assessing Unmet Information Needs of Breast Cancer Survivors: Exploratory Study of Online Health Forums Using Text Classification and Retrieval McRoy, Susan Rastegar-Mojarad, Majid Wang, Yanshan Ruddy, Kathryn J Haddad, Tufia C Liu, Hongfang JMIR Cancer Original Paper BACKGROUND: Patient education materials given to breast cancer survivors may not be a good fit for their information needs. Needs may change over time, be forgotten, or be misreported, for a variety of reasons. An automated content analysis of survivors' postings to online health forums can identify expressed information needs over a span of time and be repeated regularly at low cost. Identifying these unmet needs can guide improvements to existing education materials and the creation of new resources. OBJECTIVE: The primary goals of this project are to assess the unmet information needs of breast cancer survivors from their own perspectives and to identify gaps between information needs and current education materials. METHODS: This approach employs computational methods for content modeling and supervised text classification to data from online health forums to identify explicit and implicit requests for health-related information. Potential gaps between needs and education materials are identified using techniques from information retrieval. RESULTS: We provide a new taxonomy for the classification of sentences in online health forum data. 260 postings from two online health forums were selected, yielding 4179 sentences for coding. After annotation of data and training alternative one-versus-others classifiers, a random forest-based approach achieved F1 scores from 66% (Other, dataset2) to 90% (Medical, dataset1) on the primary information types. 136 expressions of need were used to generate queries to indexed education materials. Upon examination of the best two pages retrieved for each query, 12% (17/136) of queries were found to have relevant content by all coders, and 33% (45/136) were judged to have relevant content by at least one. CONCLUSIONS: Text from online health forums can be analyzed effectively using automated methods. Our analysis confirms that breast cancer survivors have many information needs that are not covered by the written documents they typically receive, as our results suggest that at most a third of breast cancer survivors’ questions would be addressed by the materials currently provided to them. JMIR Publications 2018-05-15 /pmc/articles/PMC5974460/ /pubmed/29764801 http://dx.doi.org/10.2196/cancer.9050 Text en ©Susan McRoy, Majid Rastegar-Mojarad, Yanshan Wang, Kathryn J. Ruddy, Tufia C. Haddad, Hongfang Liu. Originally published in JMIR Cancer (http://cancer.jmir.org), 15.05.2018. 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 Cancer, is properly cited. The complete bibliographic information, a link to the original publication on http://cancer.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
McRoy, Susan
Rastegar-Mojarad, Majid
Wang, Yanshan
Ruddy, Kathryn J
Haddad, Tufia C
Liu, Hongfang
Assessing Unmet Information Needs of Breast Cancer Survivors: Exploratory Study of Online Health Forums Using Text Classification and Retrieval
title Assessing Unmet Information Needs of Breast Cancer Survivors: Exploratory Study of Online Health Forums Using Text Classification and Retrieval
title_full Assessing Unmet Information Needs of Breast Cancer Survivors: Exploratory Study of Online Health Forums Using Text Classification and Retrieval
title_fullStr Assessing Unmet Information Needs of Breast Cancer Survivors: Exploratory Study of Online Health Forums Using Text Classification and Retrieval
title_full_unstemmed Assessing Unmet Information Needs of Breast Cancer Survivors: Exploratory Study of Online Health Forums Using Text Classification and Retrieval
title_short Assessing Unmet Information Needs of Breast Cancer Survivors: Exploratory Study of Online Health Forums Using Text Classification and Retrieval
title_sort assessing unmet information needs of breast cancer survivors: exploratory study of online health forums using text classification and retrieval
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5974460/
https://www.ncbi.nlm.nih.gov/pubmed/29764801
http://dx.doi.org/10.2196/cancer.9050
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