Text-Mining of Online Discourse to Characterize the Nature of Pain in Low Back Pain

Young Uk Ryu (류영욱)

doi:10.13066/kspm.2019.14.3.55

Text-Mining of Online Discourse to Characterize the Nature of Pain in Low Back Pain

Journal of the Korean Society of Physical Medicine
Abbr : J Korean Soc Phys Med
2019, 14(3), pp.55-62
DOI : 10.13066/kspm.2019.14.3.55
Publisher : The Korean Society of Physical Medicine
Research Area : Medicine and Pharmacy > Physical Therapy > Other physical therapy
Received : June 6, 2019
Accepted : August 12, 2019
Published : August 31, 2019

Young Uk Ryu ¹

¹대구가톨릭대학교

Accredited

ABSTRACT

PURPOSE: Text-mining has been shown to be useful for understanding the clinical characteristics and patients’ concerns regarding a specific disease. Low back pain (LBP) is the most common disease in modern society and has a wide variety of causes and symptoms. On the other hand, it is difficult to understand the clinical characteristics and the needs as well as demands of patients with LBP because of the various clinical characteristics. This study examined online texts on LBP to determine of text-mining can help better understand general characteristics of LBP and its specific elements. METHODS: Online data from www.spine-health.com were used for text-mining. Keyword frequency analysis was performed first on the complete text of postings (full-text analysis). Only the sentences containing the highest frequency word, pain, were selected. Next, texts including the sentences were used to re-analyze the keyword frequency (pain-text analysis). RESULTS: Keyword frequency analysis showed that pain is of utmost concern. Full-text analysis was dominated by structural, pathological, and therapeutic words, whereas pain-text analysis was related mainly to the location and quality of the pain. CONCLUSION: The present study indicated that text-mining for a specific element (keyword) of a particular disease could enhance the understanding of the specific aspect of the disease. This suggests that a consideration of the text source is required when interpreting the results. Clinically, the present results suggest that clinicians pay more attention to the pain a patient is experiencing, and provide information based on medical knowledge.

KEYWORDS

Text-mining, Low Back Pain, Pain, Patient Web Portal

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* References for papers published after 2023 are currently being built.

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Journal of the Korean Society of Physical Medicine 2023 KCI Impact Factor : 0.45

Text-Mining of Online Discourse to Characterize the Nature of Pain in Low Back Pain

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* References for papers published after 2023 are currently being built.

Journal of the Korean Society of Physical Medicine 2023 KCI Impact Factor : 0.45

Text-Mining of Online Discourse to Characterize the Nature of Pain in Low Back Pain

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