Applying Deep Learning in Word Embedding for Making a Diagnosis Prediction Model from Orthopedic Clinical Note

Abstract

We propose deep learning in word embedding for making a diagnostic prediction model. One factor that causes uncertainties in diagnostic is the inexperience of physicians. The diagnosis errors lead to incorrect and delay in treatment, waste of time and money. To solve the problem, a differential diagnosis is a critical tool. It is powerful and does not introduce additional work to physician. Our method applied a deep learning tool together with word embedding from existing diagnosis texts in medical system. The model takes the clinical notes from a physician. The note is then used to analyze the possibilities of diseases. The output is sorted by model confidence. We validate our model with True Positive Rate (Recall), False Positive Rate (Precision) and accuracy. Our model achieves a new record of accuracy at 99.95% The highest recall rate is at 86.64% in top first prediction.