Normalized Google Distance in the Identification and Characterization of Health Queries

Lopes, Carla Teixeira; Moura, Diogo

doi:10.23919/cisti.2019.8760964

Cited by 2 publications

(1 citation statement)

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“…Moura [124] investigate the use of Support Vector Machine (SVM) and the Normalized Google Distance metric to identify health queries (i.e., a binary classifier: is a health query or not a health query) and contextualizing health queries (i.e., determining the severity and the semantic type of health conditions that are mentioned in health queries). These methods can be applied prior to our health query expansion method to identify the health queries to be expanded.…”

Section: Automated Methods To Identify Health Search Queries Do Existmentioning

confidence: 99%

Search engines that help people make better health decisions

Jimmy¹

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The majority of Internet users access health information online using commercial search engines; sometimes preferring search over consultation with doctors and nurses. While the general public trust online health advice, and believe they are effective searchers, studies have reported that most people actually fail to subsequently make correct health decisions based on their search activities. This is concerning as less than two-thirds of those who search online to make health decisions then go on to consult a health professional with regard to their health condition. This thesis aims to create and validate methods to help the general public searching for health advice online (we refer to this search activity as consumer health search). Specifically, we investigate methods to assist people in formulating better queries and more effective appraisal Financial support My doctoral study is sponsored by the Indonesia Endowment Fund for Education (Lembaga Pengelola Dana Pendidikan / LPDP) (20151022014644).

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Section: Automated Methods To Identify Health Search Queries Do Existmentioning

confidence: 99%