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A Novel Data-Mining Approach Leveraging Social Media to Monitor Consumer Opinion of Sitagliptin
KTH, School of Technology and Health (STH), Health Systems Engineering, Systems Safety and Management.
KTH, School of Technology and Health (STH), Health Systems Engineering, Systems Safety and Management.ORCID iD: 0000-0002-1929-135X
2015 (English)In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 19, no 1, 389-396 p.Article in journal (Refereed) Published
Abstract [en]

A novel data mining method was developed to gauge the experience of the drug Sitagliptin (trade name Januvia) by patients with diabetes mellitus type 2. To this goal, we devised a two-step analysis framework. Initial exploratory analysis using self-organizing maps was performed to determine structures based on user opinions among the forum posts. The results were a compilation of user's clusters and their correlated (positive or negative) opinion of the drug. Subsequent modeling using network analysis methods was used to determine influential users among the forum members. These findings can open new avenues of research into rapid data collection, feedback, and analysis that can enable improved outcomes and solutions for public health and important feedback for the manufacturer.

Place, publisher, year, edition, pages
2015. Vol. 19, no 1, 389-396 p.
Keyword [en]
Data mining, network analysis, self-organizing map, social media
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Information Systems
Identifiers
URN: urn:nbn:se:kth:diva-159234DOI: 10.1109/JBHI.2013.2295834ISI: 000347342300046Scopus ID: 2-s2.0-84920903503OAI: oai:DiVA.org:kth-159234DiVA: diva2:783622
Note

QC 20150128

Available from: 2015-01-26 Created: 2015-01-26 Last updated: 2017-12-05Bibliographically 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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Erlandsson, Björn-Erik

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