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Online Discourse on Fibromyalgia: Text-Mining to Identify Clinical Distinction and Patient Concerns

BACKGROUND: The purpose of this study was to evaluate the possibility of using text-mining to identify clinical distinctions and patient concerns in online memoires posted by patients with fibromyalgia (FM). MATERIAL/METHODS: A total of 399 memoirs were collected from an FM group website. The unstru...

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Autores principales: Park, Jungsik, Ryu, Young Uk
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Scientific Literature, Inc. 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4199397/
https://www.ncbi.nlm.nih.gov/pubmed/25287854
http://dx.doi.org/10.12659/MSM.890793
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author Park, Jungsik
Ryu, Young Uk
author_facet Park, Jungsik
Ryu, Young Uk
author_sort Park, Jungsik
collection PubMed
description BACKGROUND: The purpose of this study was to evaluate the possibility of using text-mining to identify clinical distinctions and patient concerns in online memoires posted by patients with fibromyalgia (FM). MATERIAL/METHODS: A total of 399 memoirs were collected from an FM group website. The unstructured data of memoirs associated with FM were collected through a crawling process and converted into structured data with a concordance, parts of speech tagging, and word frequency. We also conducted a lexical analysis and phrase pattern identification. After examining the data, a set of FM-related keywords were obtained and phrase net relationships were set through a web-based visualization tool. RESULTS: The clinical distinction of FM was verified. Pain is the biggest issue to the FM patients. The pains were affecting body parts including ‘muscles,’ ‘leg,’ ‘neck,’ ‘back,’ ‘joints,’ and ‘shoulders’ with accompanying symptoms such as ‘spasms,’ ‘stiffness,’ and ‘aching,’ and were described as ‘sever,’ ‘chronic,’ and ‘constant.’ This study also demonstrated that it was possible to understand the interests and concerns of FM patients through text-mining. FM patients wanted to escape from the pain and symptoms, so they were interested in medical treatment and help. Also, they seemed to have interest in their work and occupation, and hope to continue to live life through the relationships with the people around them. CONCLUSIONS: This research shows the potential for extracting keywords to confirm the clinical distinction of a certain disease, and text-mining can help objectively understand the concerns of patients by generalizing their large number of subjective illness experiences. However, it is believed that there are limitations to the processes and methods for organizing and classifying large amounts of text, so these limits have to be considered when analyzing the results. The development of research methodology to overcome these limitations is greatly needed.
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spelling pubmed-41993972014-10-16 Online Discourse on Fibromyalgia: Text-Mining to Identify Clinical Distinction and Patient Concerns Park, Jungsik Ryu, Young Uk Med Sci Monit Special Reports BACKGROUND: The purpose of this study was to evaluate the possibility of using text-mining to identify clinical distinctions and patient concerns in online memoires posted by patients with fibromyalgia (FM). MATERIAL/METHODS: A total of 399 memoirs were collected from an FM group website. The unstructured data of memoirs associated with FM were collected through a crawling process and converted into structured data with a concordance, parts of speech tagging, and word frequency. We also conducted a lexical analysis and phrase pattern identification. After examining the data, a set of FM-related keywords were obtained and phrase net relationships were set through a web-based visualization tool. RESULTS: The clinical distinction of FM was verified. Pain is the biggest issue to the FM patients. The pains were affecting body parts including ‘muscles,’ ‘leg,’ ‘neck,’ ‘back,’ ‘joints,’ and ‘shoulders’ with accompanying symptoms such as ‘spasms,’ ‘stiffness,’ and ‘aching,’ and were described as ‘sever,’ ‘chronic,’ and ‘constant.’ This study also demonstrated that it was possible to understand the interests and concerns of FM patients through text-mining. FM patients wanted to escape from the pain and symptoms, so they were interested in medical treatment and help. Also, they seemed to have interest in their work and occupation, and hope to continue to live life through the relationships with the people around them. CONCLUSIONS: This research shows the potential for extracting keywords to confirm the clinical distinction of a certain disease, and text-mining can help objectively understand the concerns of patients by generalizing their large number of subjective illness experiences. However, it is believed that there are limitations to the processes and methods for organizing and classifying large amounts of text, so these limits have to be considered when analyzing the results. The development of research methodology to overcome these limitations is greatly needed. International Scientific Literature, Inc. 2014-10-07 /pmc/articles/PMC4199397/ /pubmed/25287854 http://dx.doi.org/10.12659/MSM.890793 Text en © Med Sci Monit, 2014 This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License
spellingShingle Special Reports
Park, Jungsik
Ryu, Young Uk
Online Discourse on Fibromyalgia: Text-Mining to Identify Clinical Distinction and Patient Concerns
title Online Discourse on Fibromyalgia: Text-Mining to Identify Clinical Distinction and Patient Concerns
title_full Online Discourse on Fibromyalgia: Text-Mining to Identify Clinical Distinction and Patient Concerns
title_fullStr Online Discourse on Fibromyalgia: Text-Mining to Identify Clinical Distinction and Patient Concerns
title_full_unstemmed Online Discourse on Fibromyalgia: Text-Mining to Identify Clinical Distinction and Patient Concerns
title_short Online Discourse on Fibromyalgia: Text-Mining to Identify Clinical Distinction and Patient Concerns
title_sort online discourse on fibromyalgia: text-mining to identify clinical distinction and patient concerns
topic Special Reports
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4199397/
https://www.ncbi.nlm.nih.gov/pubmed/25287854
http://dx.doi.org/10.12659/MSM.890793
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