  Mining social media messages for health and drug related information has
received significant interest in pharmacovigilance research. Social media sites
(e.g., Twitter), have been used for monitoring drug abuse, adverse reactions of
drug usage and analyzing expression of sentiments related to drugs. Most of
these studies are based on aggregated results from a large population rather
than specific sets of individuals. In order to conduct studies at an individual
level or specific cohorts, identifying posts mentioning intake of medicine by
the user is necessary. Towards this objective, we train different deep neural
network classification models on a publicly available annotated dataset and
study their performances on identifying mentions of personal intake of medicine
in tweets. We also design and train a new architecture of a stacked ensemble of
shallow convolutional neural network (CNN) ensembles. We use random search for
tuning the hyperparameters of the models and share the details of the values
taken by the hyperparameters for the best learnt model in different deep neural
network architectures. Our system produces state-of-the-art results, with a
micro- averaged F-score of 0.693.
