Can Text-Mining Be Used to Identify Common Symptoms and Concerns of Fibromyalgia Patients?


Editor’s comment: Text mining is a process that analyzes data found within natural language text. Basically, it turns text into numbers. In the case of this study, researchers used text mining to analyze the personal stories of 399 fibromyalgia patients collected from a fibromyalgia group website. By identifying word patterns and picking out keywords from the stories, they were able to determine the most common symptoms patients were experiencing, their greatest concerns and what they were most interested in.

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 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.

Source: Medical Science Monitor, October 7, 2014. By J. Park and Y.U. Ryu. College of Humanities, Ajou University, Suwon-si, Korea; Department of Physical Therapy, Catholic University of Daegu, Gyeongsan-si, Korea.

1 Star2 Stars3 Stars4 Stars5 Stars (2 votes, average: 3.00 out of 5)

Leave a Reply