Learning from the Experience of Doctors: Automated Diagnosis of Appendicitis Based on Clinical Notes

The objective of this work is to develop an automated diagnosis system that is able to predict the probability of appendicitis given a free-text emergency department (ED) note and additional structured information (e.g., lab test results). Our clinical corpus consists of about 180,000 ED notes based on ten years of patient visits to the Accident and Emergency (A{\&}E) Department of the National University Hospital (NUH), Singapore. We propose a novel neural network approach that learns to diagnose acute appendicitis based on doctors{'} free-text ED notes without any feature engineering. On a test set of 2,000 ED notes with equal number of appendicitis (positive) and non-appendicitis (negative) diagnosis and in which all the negative ED notes only consist of abdominal-related diagnosis, our model is able to achieve a promising F{\_}0.5-score of 0.895 while ED doctors achieve F{\_}0.5-score of 0.900. Visualization shows that our model is able to learn important features, signs, and symptoms of patients from unstructured free-text ED notes, which will help doctors to make better diagnosis.