Team UKNLP: Detecting ADRs, classifying medication intake messages, and normalizing ADR mentions on twitter

Sifei Han, Tung Tran, Anthony Rios, Ramakanth Kavuluru

Research output: Contribution to journalConference articlepeer-review

15 Scopus citations


This paper describes the systems we developed for all three tasks of the 2nd Social Media Mining for Health Applications Shared Task at AMIA 2017. The first task focuses on identifying the Twitter posts containing mentions of adverse drug reactions (ADR). The second task focuses on automatic classification of medication intake messages (among those containing drug names) on Twitter. The last task is on identifying the MEDDRA Preferred Term (PT) code for the ADR mentions expressed in casual social text. We propose convolutional neural network (CNN) and traditional linear model (TLM) approaches for the first and second tasks and use hierarchical long short-term memory (LSTM) recurrent neural networks for the third task. Among 11 teams our systems ranked 4th in ADR detection with F-score 40.2% and 2nd in classifying medication intake messages with F-score 68.9%. For the MEDDRA PT code identification, we obtained an accuracy of 87.2%, which is nearly 1% lower than the top score from the only other team that participated.

Original languageEnglish
Pages (from-to)49-53
Number of pages5
JournalCEUR Workshop Proceedings
StatePublished - 2017
Event2nd Social Media Mining for Health Research and Applications Workshop, SMM4H 2017 - Washington, United States
Duration: Nov 4 2017 → …

Bibliographical note

Funding Information:
Our work is primarily supported by the National Library of Medicine through grant R21LM012274 and the National Cancer Institute through grant R21CA218231.

ASJC Scopus subject areas

  • Computer Science (all)


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