Sifei Han, Tung Tran, Anthony Rios, and Ramakanth Kavuluru

In Proceedings of the 2nd Social Media Mining for Health Applications workshop at AMIA 2017.


This paper describes the systems we developed for all three tasks of the 2nd Social Media Mining for Health Ap- plications 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 tra- ditional 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.


  author    = {Sifei Han and
               Tung Tran and
               Anthony Rios and
               Ramakanth Kavuluru},
  title     = {Team {UKNLP:} Detecting ADRs, Classifying Medication Intake Messages,
               and Normalizing {ADR} Mentions on Twitter},
  booktitle = {Proceedings of the 2nd Social Media Mining for Health Research and
               Applications Workshop co-located with the American Medical Informatics
               Association Annual Symposium {(AMIA} 2017), Washington D.C., United
               States, November 4, 2017.},
  pages     = {49--53},
  year      = {2017},
  crossref  = {DBLP:conf/amia/2017smm4h},
  url       = {},
  timestamp = {Thu, 18 Jan 2018 18:37:36 +0100},
  biburl    = {},
  bibsource = {dblp computer science bibliography,}