Analysis of online information searching for cardiovascular diseases on a consumer health information portal

Ashutosh Jadhav, Amit Sheth, Jyotishman Pathak

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Since the early 2000's, Internet usage for health information searching has increased significantly. Studying search queries can help us to understand users "information need" and how do they formulate search queries ("expression of information need"). Although cardiovascular diseases (CVD) affect a large percentage of the population, few studies have investigated how and what users search for CVD. We address this knowledge gap in the community by analyzing a large corpus of 10 million CVD related search queries from MayoClinic.com. Using UMLS MetaMap and UMLS semantic types/concepts, we developed a rule-based approach to categorize the queries into 14 health categories. We analyzed structural properties, types (keyword-based/Wh-questions/Yes-No questions) and linguistic structure of the queries. Our results show that the most searched health categories are 'Diseases/Conditions', 'Vital-Sings', 'Symptoms' and 'Living-with'. CVD queries are longer and are predominantly keyword-based. This study extends our knowledge about online health information searching and provides useful insights for Web search engines and health websites.

Original languageEnglish (US)
Pages (from-to)739-748
Number of pages10
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
Volume2014
StatePublished - 2014

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Consumer Health Information
Cardiovascular Diseases
Unified Medical Language System
Health
Search Engine
Linguistics
Semantics
Internet
Population

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Analysis of online information searching for cardiovascular diseases on a consumer health information portal. / Jadhav, Ashutosh; Sheth, Amit; Pathak, Jyotishman.

In: AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium, Vol. 2014, 2014, p. 739-748.

Research output: Contribution to journalArticle

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