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Mining Social Media Big Data for Health
KTH, School of Technology and Health (STH), Health Systems Engineering, Systems Safety and Management.
University of Houston, Biomedical Engineering.
KTH, School of Technology and Health (STH), Health Systems Engineering.ORCID iD: 0000-0002-1929-135X
2015 (English)In: IEEE PulseArticle, review/survey (Refereed) Published
Abstract [en]

Advances in information technology (IT) and big data are affecting nearly every facet of the public and private sectors. Social media platforms are one example of such advances: its nature allows users to connect, collaborate, and debate on any topic with comparative ease. The result is a hefty volume of user-generated content that, if properly mined and analyzed, could help the public and private health care sectors improve the quality of their products and services while reducing costs. The users of these platforms are the key to these improvements, as their valuable feedback will help improve health solutions.

Place, publisher, year, edition, pages
2015.
National Category
Public Health, Global Health, Social Medicine and Epidemiology
Research subject
Medical Technology
Identifiers
URN: urn:nbn:se:kth:diva-180349OAI: oai:DiVA.org:kth-180349DiVA: diva2:893113
Note

QC 20160112

Available from: 2016-01-12 Created: 2016-01-12 Last updated: 2017-03-13Bibliographically approved
In thesis
1. A Novel Method to Intelligently Mine Social Media to Assess Consumer Sentiment of Pharmaceutical Drugs
Open this publication in new window or tab >>A Novel Method to Intelligently Mine Social Media to Assess Consumer Sentiment of Pharmaceutical Drugs
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis focuses on the development of novel data mining techniques that convert user interactions in social media networks into readable data that would benefit users, companies, and governments. The readable data can either warn of dangerous side effects of pharmaceutical drugs or improve intervention strategies. A weighted model enabled us to represent user activity in the network, that allowed us to reflect user sentiment of a pharmaceutical drug and/or service. The result is an accurate representation of user sentiment. This approach, when modified for specific diseases, drugs, and services, can enable rapid user feedback that can be converted into rapid responses from consumers to industry and government to withdraw possibly dangerous drugs and services from the market or improve said drugs and services.

Our approach monitors social media networks in real-time, enabling government and industry to rapidly respond to consumer sentiment of pharmaceutical drugs and services.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2017. 34 p.
Keyword
Data Mining
National Category
Other Medical Engineering
Research subject
Medical Technology
Identifiers
urn:nbn:se:kth:diva-203119 (URN)978-91-7729-295-1 (ISBN)
Public defence
2017-03-22, Hälsovägen 11C, Huddinge, 10:00 (English)
Supervisors
Note

QC 20170314

Available from: 2017-03-14 Created: 2017-03-11 Last updated: 2017-03-14Bibliographically approved

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