Quantifying self-reported adverse drug events on Twitter: Signal and topic analysis

ACM International Conference Proceeding Series

Volumn , Issue , 2016, Pages

  Plachouras, Vassilis ^a   Leidner, Jochen L ^a   Garrow, Andrew G ^a  

^a THOMSON REUTERS   (United Kingdom)

Author keywords

Classification; Drug side effects; Natural Language Processing; Text Mining; Twitter

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA MINING; DRUG INTERACTIONS; ECOSYSTEMS; FILTRATION; INTERACTIVE COMPUTER SYSTEMS; LEARNING ALGORITHMS; NATURAL LANGUAGE PROCESSING SYSTEMS; PIPELINE PROCESSING SYSTEMS; REAL TIME SYSTEMS; SUPERVISED LEARNING; SUPPORT VECTOR MACHINES; TEXT PROCESSING;

ADVERSE DRUG EVENTS; DRUG SIDE EFFECTS; HEALTH CARE PROFESSIONALS; PHARMACEUTICAL COMPANY; PHARMACEUTICAL DRUGS; SUPERVISED MACHINE LEARNING; TEXT MINING; TWITTER;

SOCIAL NETWORKING (ONLINE);

EID: 85018438197     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2930971.2930977     Document Type: Conference Paper

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