Using recurrent neural networks to predict colorectal cancer among patients

Development of predictive models from Electronic Medical Records (EMRs) is a far from trivial task. Especially the temporal nature of health records is an aspect that is often ignored yet of utmost importance. Additionally, data is extremely sparse. Previous research has shown that the identification of temporal patterns from EMR data can be highly beneficial in the prediction of colorectal cancer (CRC). In this paper, we try to apply recurrent neural networks, and more specifically Long Short Term Memory (LSTM) networks and Gated Recurrent Units (GRUs) to see whether these networks could learn such valuable temporal patterns themselves and generate accurate predictive models for CRC. Results show that we attain performance on par with state-of-the-art algorithms (while being outperformed by one). The eventual Area under the ROC Curve (AUC) obtained is 0.811.