Deep Learning Model for Early Subsequent COPD Exacerbation Prediction

Chronic Obstructive Pulmonary Disease (COPD) patients have a burden of frequent exacerbations during daily life. Automatic solutions for early COPD exacerbation prediction could promote COPD healthcare and reduce hospital readmissions. Previous works didn't consider symptoms change patterns which might not be effective for timely and personalized therapy. When using a pulse oximeter for COPD diagnosis, arterial oxygen saturation (SPO2) levels are targeted, depending on whether a patient is stable, hospitalized, or being recovered from exacerbation states. However, the timely management of COPD is a problem, due to the manual monitoring of individual measurements. This research investigates whether the Long Short-Term Memory (LSTM) model can predict early COPD subsequent exacerbation by prompting therapy depending on COPD symptoms patterns and SPO2 burden levels. Time-stamped Electronic Health Record (EHR) from COPD patients' data time series were examined, over subsequent days, with the aim to evaluate a short-time window which a monitoring system for an accurate and early prediction of subsequent exacerbations could be based on. Therefore, the LSTM model was evaluated by varying a window of one to six prior time-steps, to forecast a subsequent day. The window of 1 day showed a good performance of a training accuracy of 87%, a testing accuracy of 85% and an area under the curve (AUC) of 0.83, by employing the training and testing model on only 54 patients.

Automated summary

This study investigates the prediction of early COPD exacerbation using the Long Short-Term Memory (LSTM) model, considering COPD symptom patterns and SPO2 burden levels. The analysis of time-stamped Electronic Health Record (EHR) data from COPD patients shows that a one-day window has a good performance in predicting subsequent exacerbations.