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Assessing Antidepressants Using Intelligent Data Monitoring and Mining of Online Fora
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
2016 (English)In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 20, no 4, 977-986 p.Article in journal (Refereed) Published
Resource type
Text
Abstract [en]

Depression is a global health concern. Social networks allow the affected population to share their experiences. These experiences, when mined, extracted, and analyzed, can be converted into either warnings to recall drugs (dangerous side effects), or service improvement (interventions, treatment options) based on observations derived from user behavior in depression-related social networks. Our aim was to develop a weighted network model to represent user activity on social health networks. This enabled us to accurately represent user interactions by relying on the data's semantic content. Our three-step method uses the weighted network model to represent user's activity, and network clustering and module analysis to characterize user interactions and extract further knowledge from user's posts. The network's topological properties reflect user activity such as posts' general topic as well as timing, while weighted edges reflect the posts semantic content and similarities among posts. The result, a synthesis from word data frequency, statistical analysis of module content, and the modeled health network's properties, has allowed us to gain insight into consumer sentiment of antidepressants. This approach will allow all parties to participate in improving future health solutions of patients suffering from depression.

Place, publisher, year, edition, pages
IEEE , 2016. Vol. 20, no 4, 977-986 p.
Keyword [en]
Data mining, depression, network analysis, online fora, semantic analysis, social media, user sentiment
National Category
Health Sciences Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-190560DOI: 10.1109/JBHI.2016.2539972ISI: 000380128300002PubMedID: 27164611Scopus ID: 2-s2.0-84978285828OAI: oai:DiVA.org:kth-190560DiVA: diva2:952580
Note

QC 20160815

Available from: 2016-08-15 Created: 2016-08-12 Last updated: 2017-03-11Bibliographically 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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