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Online discourse on fibromyalgia: text-mining to identify clinical distinction and patient concerns.

Park J, Ryu YU - Med. Sci. Monit. (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: College of Humanities, Ajou University, Suwon-si, Korea.

ABSTRACT

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 and 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.

Show MeSH

Related in: MedlinePlus

The Phrase Net: the keywords before/after ‘pain’. The Phrase Net that shows the keywords that were directly preceding or following the word ‘pain’. The main keyword “pain” is not scaled proportionally here but others are. The original Phrase Net is modified here for better resolution of the view.
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f3-medscimonit-20-1858: The Phrase Net: the keywords before/after ‘pain’. The Phrase Net that shows the keywords that were directly preceding or following the word ‘pain’. The main keyword “pain” is not scaled proportionally here but others are. The original Phrase Net is modified here for better resolution of the view.

Mentions: For more detailed information about the use of ‘pain,’ we conducted 2 additional analyses: 1) keywords that were frequently found in the same sentence as the word ‘pain’ as in “I experience chronic pain, debilitating muscle spasms, joint tenderness and swelling” (see Figures 2 and 3), and keywords that were directly preceding or following the word ‘pain’ as in “It’s so hard to explain to someone that doesn’t have chronic pain,” and “I still am waiting for that elusive pain-free day” (Figure 3). By using the concordance and phrase net visualization, we obtained more concrete information related to the ‘pain’ of FM. Specifically, the pain of FM seems to occur mainly in the body (9th), muscles (15th), neck (46th), back (60th), joints (62nd), and shoulders (68th) (Figures 2 and 3). Fatigue (34th) and depression (33rd) also seem to be related with pain (Figure 2). The aspects associated with the pain are ‘spasms’, ‘stiffness’, and ‘aching’ (Figure 2), although these nouns were not in the top 100. Overall, the pain is described as being ‘severe,’ ‘chronic,’ and ‘constant’ (Figure 3).


Online discourse on fibromyalgia: text-mining to identify clinical distinction and patient concerns.

Park J, Ryu YU - Med. Sci. Monit. (2014)

The Phrase Net: the keywords before/after ‘pain’. The Phrase Net that shows the keywords that were directly preceding or following the word ‘pain’. The main keyword “pain” is not scaled proportionally here but others are. The original Phrase Net is modified here for better resolution of the view.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4199397&req=5

f3-medscimonit-20-1858: The Phrase Net: the keywords before/after ‘pain’. The Phrase Net that shows the keywords that were directly preceding or following the word ‘pain’. The main keyword “pain” is not scaled proportionally here but others are. The original Phrase Net is modified here for better resolution of the view.
Mentions: For more detailed information about the use of ‘pain,’ we conducted 2 additional analyses: 1) keywords that were frequently found in the same sentence as the word ‘pain’ as in “I experience chronic pain, debilitating muscle spasms, joint tenderness and swelling” (see Figures 2 and 3), and keywords that were directly preceding or following the word ‘pain’ as in “It’s so hard to explain to someone that doesn’t have chronic pain,” and “I still am waiting for that elusive pain-free day” (Figure 3). By using the concordance and phrase net visualization, we obtained more concrete information related to the ‘pain’ of FM. Specifically, the pain of FM seems to occur mainly in the body (9th), muscles (15th), neck (46th), back (60th), joints (62nd), and shoulders (68th) (Figures 2 and 3). Fatigue (34th) and depression (33rd) also seem to be related with pain (Figure 2). The aspects associated with the pain are ‘spasms’, ‘stiffness’, and ‘aching’ (Figure 2), although these nouns were not in the top 100. Overall, the pain is described as being ‘severe,’ ‘chronic,’ and ‘constant’ (Figure 3).

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: College of Humanities, Ajou University, Suwon-si, Korea.

ABSTRACT

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 and 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.

Show MeSH
Related in: MedlinePlus