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Mining Social Media Big Data for Health
KTH, Skolan för teknik och hälsa (STH), Hälso- och systemvetenskap, Systemsäkerhet och organisation.
University of Houston, Biomedical Engineering.
KTH, Skolan för teknik och hälsa (STH), Hälso- och systemvetenskap.ORCID-id: 0000-0002-1929-135X
2015 (engelsk)Inngår i: IEEE PulseArtikkel, forskningsoversikt (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
2015.
HSV kategori
Forskningsprogram
Medicinsk teknologi
Identifikatorer
URN: urn:nbn:se:kth:diva-180349OAI: oai:DiVA.org:kth-180349DiVA, id: diva2:893113
Merknad

QC 20160112

Tilgjengelig fra: 2016-01-12 Laget: 2016-01-12 Sist oppdatert: 2017-03-13bibliografisk 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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Erlandsson, Björn-Erik

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