Deep EHR: Chronic Disease Prediction Using Medical Notes
Table 2: Model Performance (AUC) by Target Disease
['[EMPTY]', '[BOLD] Heart Failure', '[BOLD] Kidney Failure', '[BOLD] Stroke']
Utilizing all available notes except for those on patients in the validation or the test set, we obtain a much larger embedding training set than that of the prediction task. We test a set of 300-dimension embeddings and 50-dimension ones early in the process and found the former outperformed the latter in the prediction tasks. We use the 300-dimension ones as input for all the deep learning models discussed in later sections. Multitask learning has shown to be an effective method of regularization in many deep learning applications. We compare the multitask approach with training each model separately with the CNN model. To address this problem we tag all negated phrases through a simple prepossessing step. We then take the negative of the original word embedding as the embedding of each negated token. To tag negations, we use the Negex system[Chapman et al. chapman2001simple], which is a regular expression based system optimized for tagging negation in medical text. We see consistent improvement of prediction score across all our architectures. Below is an example of how adding negation tags corrects a false positive for stroke prediction. Nevertheless, it should be noted that the accuracy of negation tagging is a bottleneck in this project and more value could potentially be added with a more sophisticated negation tagging model.