Comparison of Convolutional Neural Network Architectures and their Influence on Patient Classification Tasks Relating to Altered Mental Status.

Abstract

With the pervasiveness of Electronic Health Records in many hospital systems, the application of machine learning techniques to the field of health informatics has become much more feasible as large amounts of data become more accessible. In our experiment, we evaluated several different convolutional neural network architectures that are typically used in text classification tasks. We then tested those models based on 1,113 histories of present illness. (HPI) notes. This data was run over both sequential and multi-channel architectures, as well as a structure that implemented attention methods meant to focus the model on learning the influential data points within the text. We found that the multi-channel model performed the best with an accuracy of 92%, while the attention and sequential models performed worse with an accuracy of 90% and 89% respectively.