Mention extraction and normalization using ontologies in the biomedical domain

This thesis proposes a machine learning- and rule-based system for the identification of adverse drug reaction (ADR) entity mentions in the text of drug labels and their normalization through the MedDRA dictionary. The machine learning approach is based on a recently proposed deep learning model that works on the sentence level. The model makes use of the combination of the pre-trained word embeddings and Convolutional Neural Network (CNN) embeddings generated from the characters of a given token. These tokens are initially passed through bi-directional Long Short-Term Memory (Bi-LSTM) layers for feature extraction. Finally, a Conditional Random Fields (CRF) classifier is trained on those extracted features for the prediction of the target mentions. The rule-based approach, used for normalizing the identified ADR mentions to MedDRA terms, is based on an extension of the text-mining system called SciMiner. The proposed system is evaluated with the TAC-ADR 2017 challenge dataset. Since this dataset contains mentions that are disjoint and overlapping, the model also uses a recently proposed chunking scheme designed to handle those types. The model obtained 76.97 f-score performance on the TAC dataset. Some of the challenges for the worse performance compared to performance of the models trained on the generic newspaper text are the small size of the training dataset and the uneven distribution of the class instances.