Menopause and big data: Word Adjacency Graph modeling of menopause-related ChaCha data. Issue 7 (July 2017)

• Record Type: Journal Article
• Authors: Carpenter, Janet S.; Groves, Doyle; Chen, Chen X.; Otte, Julie L.; Miller, Wendy R.
• Is Part Of: Menopause. Volume 24:Issue 7(2017)
• Publication Date: 2017-07
• Subjects: Data mining -- Hormone therapy -- Hot flashes -- Information science -- Menopause -- Symptoms; Menopause -- Periodicals; 618.175005
• DOI: 10.1097/GME.0000000000000833
• Languages: English
• ISSNs: 1072-3714

Abstract

Abstract: Objective: To detect and visualize salient queries about menopause using Big Data from ChaCha. Methods: We used Word Adjacency Graph (WAG) modeling to detect clusters and visualize the range of menopause-related topics and their mutual proximity. The subset of relevant queries was fully modeled. We split each query into token words (ie, meaningful words and phrases) and removed stopwords (ie, not meaningful functional words). The remaining words were considered in sequence to build summary tables of words and two and three-word phrases. Phrases occurring at least 10 times were used to build a network graph model that was iteratively refined by observing and removing clusters of unrelated content. Results: We identified two menopause-related subsets of queries by searching for questions containing menopause and menopause-related terms (eg, climacteric, hot flashes, night sweats, hormone replacement). The first contained 263, 363 queries from individuals aged 13 and older and the second contained 5, 892 queries from women aged 40 to 62 years. In the first set, we identified 12 topic clusters: 6 relevant to menopause and 6 less relevant. In the second set, we identified 15 topic clusters: 11 relevant to menopause and 4 less relevant. Queries about hormones were pervasive within both WAG models. Many of the queries reflected low literacy levels and/or feelings of embarrassment. Conclusions: We modeled menopause-related queries posed by ChaCha users between 2009 and 2012. ChaCha data may be used on its own or in combination with other Big Data sources to identify patient-driven educational needs and create patient-centered interventions.