Chief complaint classification with recurrent neural networks

Understand

Syndromic surveillance detects and monitors individual and population health indicators through sources such as emergency department records.

• Automated classification of these records can improve outbreak detection speed and diagnosis accuracy.
• Current syndromic systems rely on hand-coded keyword-based methods to parse written fields and may benefit from the use of modern supervised-learning classifier models.
• In this paper we implement two recurrent neural network models based on long short-term memory (LSTM) and gated recurrent unit (GRU) cells and compare them to two traditional bag-of-words classifiers: multinomial naive Bayes (MNB) and a support vector machine (SVM).