Deep Neural Networks for Prediction of Exacerbations of Patients with Chronic Obstructive Pulmonary Disease

Chronic Obstructive Pulmonary Disease (COPD) patients need help in daily life situations as they are burdened with frequent risks of acute exacerbation and loss of control. An automated monitoring system could lead to timely treatments and avoid unnecessary hospital (re-)admissions and home visits by doctors or nurses. Therefore we present a Deep Artificial Neural Networks for approach prediction of exacerbations, particularly Feed-Forward Neural Networks (FFNN) for classification of COPD patients category and Long Short-Term Memory (LSTM), for early prediction of COPD exacerbations and subsequent triage. The FFNN and LSTM models are trained on data collected from remote monitoring of 94 patients through a real monitoring session and therefore represents realistic home monitoring situations. Most deep learning models require large datasets in order to predict with a high degree of accuracy. Our experiments show that with only 94 patients, the FFNN model is able to reproduce health condition provided by a medical doctor with an accuracy of 92.86% and the LSTM model able to predict COPD patients’ health conditions one-day ahead with an accuracy of 84.12%. Based on our results, we believe that our work will help the medical doctors and nurses in identifying patients with acute exacerbation in advance which can lead to better patient care and decision making, and hence reduction of costs.