Predicting Chronic Obstructive Pulmonary Disease from EMR data

The primary objective of this study was to develop a predictive model that used structured data from the Electronic Medical Records (EMR) to identify Chronic Obstructive Pulmonary Disease (COPD). The symptoms of COPD overlap with many other diseases, therefore it is important to identify a group of COPD symptoms that are frequently documented in the EMR. We explored seven tables from the Manitoba Primary Care Research Network (MaPCReN): patient demographics, health condition, billing, disease case, examinations, medication and risk factors. We applied three supervised machine learning models, a Multilayer Neural Networks (MLNN) model, a Support Vector Machine (SVM), and an Extreme Gradient Boosting (XGB) to identify COPD patients using EMR data. When used to predict COPD, the XGB model achieved an accuracy of 83%, compared to 81% accuracy of the SVM and 80% of the MLNN. Utilizing feature importance, we identified a set of key symptoms for diagnosing COPD within the EMR data namely medications, conditions, risk factors and patient age.