Machine Learning analysis of text in a Clinical Decision Support System

Abstract: Nurses at the Uppsala Emergency Medical Dispatch Center uses a computerized dispatcher system to prioritize patients calling the emergency number (112). The dispatchers at the emergency dispatcher center register information into that system to help them determine the treatment necessary for the patient’s condition. One thing the nurses want to find out is whether a specific patient will require admission to the hospital. In addition to structured data from the decision support system, notes written by dispatchers are documented. In this work, we have analyzed ways we can use the text from ambulance dispatchers to predict outcomes using methods that enable computers to understand natural language called Natural Language Processing, and have been implemented using machine learning approaches such as Classification and Deep Learning developed in Python, SKLearn and Keras. To perform training using our data along with these approaches, we transformed our data using three types of representations: Bag-of-words, Tf'IDF and word vectors. The aim of these representations and approaches is for our machine learning models to be able to predict the likelihood of outcomes based on a given set of data. The results from the training gave us an understanding that some models performed better than the others, but also that the imbalance of the data prevented the models from generating more accurate result.