Deep Learning Methods for Patient Phenotyping from Electronic Health Records

Abstract

In this MSc thesis we employed convolutional neural network based architectures in classifying free-form discharge summaries from electronic health records in the Medical Information Mart for Intensive Care III database. We intended to investigate how well deep learning models can perform in patient phenotyping tasks using unstructured data. We based our work on the previous work done by Gehrmann, Sebastian, et al. in their paper "Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives". We performed our tasks first by replicating their results using slightly different implementation details, then we extended the network architecture they used in their work, and finally we compared the results of our architecture and their architecture. The main work of this thesis is the extra sentence level network that we added to the network architecture we replicated. In our network architecture, we fed not only the word level but also the sentence level inputs to the networks, thus making the networks able to learn features from combinations of nearby sentences. Our experiments have shown our network architecture had a better performance over the original network architecture. It gave better results on all the F1 scores for all phenotypes, we also saw an overall improvement on ROCAUC scores. This indicates that the networks can benefit from our sentence level input to better understand the unstructured data from eHRs.