An automatic grading system for electronic medical records with neural network

Automatically grading electronic medical records from clinical researchers is an important task on healthcare research. In this paper, we present a convolutional neural network framework to grade medical records by grading note. We regard the scoring process as a text pattern classification task with mapped sentences from grading concept rule to medical records. This framework involves two stages. The first stage is key medical concept matching between grading note and medical records. The second stage is text pattern classification which can predict whether the key concept in grading note is correct, missing or incorrect. The result shows that our neural network model performs better than other traditional machine learning grading methods. Our system makes a great progress for text pattern classification accuracy and performs much better baselines on grading process.