  This paper describes Infosys's participation in the "2nd Social Media Mining
for Health Applications Shared Task at AMIA, 2017, Task 2". Mining social media
messages for health and drug related information has received significant
interest in pharmacovigilance research. This task targets at developing
automated classification models for identifying tweets containing descriptions
of personal intake of medicines. Towards this objective we train a stacked
ensemble of shallow convolutional neural network (CNN) models on an annotated
dataset provided by the organizers. We use random search for tuning the
hyper-parameters of the CNN and submit an ensemble of best models for the
prediction task. Our system secured first place among 9 teams, with a
micro-averaged F-score of 0.693.
