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Network-Based Modeling and Intelligent Data Mining of Social Media for Improving Care
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, 210-218 p.Article in journal (Refereed) Published
Abstract [en]

Intelligently extracting knowledge from social media has recently attracted great interest from the Biomedical and Health Informatics community to simultaneously improve healthcare outcomes and reduce costs using consumer-generated opinion. We propose a two-step analysis framework that focuses on positive and negative sentiment, as well as the side effects of treatment, in users' forum posts, and identifies user communities (modules) and influential users for the purpose of ascertaining user opinion of cancer treatment. We used a self-organizing map to analyze word frequency data derived from users' forum posts. We then introduced a novel network-based approach for modeling users' forum interactions and employed a network partitioning method based on optimizing a stability quality measure. This allowed us to determine consumer opinion and identify influential users within the retrieved modules using information derived from both word-frequency data and network-based properties. Our approach can expand research into intelligently mining social media data for consumer opinion of various treatments to provide rapid, up-to-date information for the pharmaceutical industry, hospitals, and medical staff, on the effectiveness (or ineffectiveness) of future treatments.

Place, publisher, year, edition, pages
2015. Vol. 19, no 1, 210-218 p.
Keyword [en]
complex networks, datamining, neural networks, semantic web, social computing
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-159235DOI: 10.1109/JBHI.2014.2336251ISI: 000347342300026Scopus ID: 2-s2.0-84920971095OAI: oai:DiVA.org:kth-159235DiVA: diva2:783629
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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