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A Novel Data-Mining Approach Leveraging Social Media to Monitor Consumer Opinion of Sitagliptin
KTH, Skolan för teknik och hälsa (STH), Hälso- och systemvetenskap, Systemsäkerhet och organisation.
KTH, Skolan för teknik och hälsa (STH), Hälso- och systemvetenskap, Systemsäkerhet och organisation.ORCID-id: 0000-0002-1929-135X
2015 (engelsk)Inngår i: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 19, nr 1, s. 389-396Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
2015. Vol. 19, nr 1, s. 389-396
Emneord [en]
Data mining, network analysis, self-organizing map, social media
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-159234DOI: 10.1109/JBHI.2013.2295834ISI: 000347342300046Scopus ID: 2-s2.0-84920903503OAI: oai:DiVA.org:kth-159234DiVA, id: diva2:783622
Merknad

QC 20150128

Tilgjengelig fra: 2015-01-26 Laget: 2015-01-26 Sist oppdatert: 2018-01-11bibliografisk kontrollert
Inngår i avhandling
1. A Novel Method to Intelligently Mine Social Media to Assess Consumer Sentiment of Pharmaceutical Drugs
Åpne denne publikasjonen i ny fane eller vindu >>A Novel Method to Intelligently Mine Social Media to Assess Consumer Sentiment of Pharmaceutical Drugs
2017 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Stockholm: KTH Royal Institute of Technology, 2017. s. 34
Emneord
Data Mining
HSV kategori
Forskningsprogram
Medicinsk teknologi
Identifikatorer
urn:nbn:se:kth:diva-203119 (URN)978-91-7729-295-1 (ISBN)
Disputas
2017-03-22, Hälsovägen 11C, Huddinge, 10:00 (engelsk)
Veileder
Merknad

QC 20170314

Tilgjengelig fra: 2017-03-14 Laget: 2017-03-11 Sist oppdatert: 2017-03-14bibliografisk kontrollert

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