Application of deep-learning for medical text analysis: a comparison of different types of recurrent neural network

We investigate the application of Deep Learning in the healthcare settings to automatically categorize symptoms using patient transcriptions. Several types of networks are investigated, including using stacked unidirectional and bidirectional long short-term memory networks and gated recurrent unit networks, and combinations of these. The approach obtains high accuracy, precision, and recall. Specifically, the stacked LSTM model, stacked bidirectional GRU model, and the stacked bidirectional LSTM model perform equally well in term of accuracy, while that of the stacked GRU model is slightly lower. These well-performing models could be applied for automatic symptom categorization based on patient transcriptions.