Saturday , May 18 2024

Deep Learning Model for Early Subsequent COPD Exacerbation Prediction

Claudia ABINEZA1, Valentina E. BALAS*2,3, Philibert NSENGIYUMVA1
1 African Center of Excellence in Internet of Things, University of Rwanda, KN 67 Street, Kigali, 3900, Rwanda
abineza1@gmail.com, nsenga_philibert@yahoo.com
2 Department of Automatics and Applied Software, Aurel Vlaicu University of Arad,
77 Revoluţiei Boulevard, Arad, 310130, Romania
valentina.balas@uav.ro
3 Academy of Romanian Scientists, 3 Ilfov Street, Bucharest, 050044, Romania
balas@drbalas.ro (*Corresponding author)

Abstract: 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.

Keywords: Early prediction, Subsequent COPD exacerbation, Data-time series, LSTM, Monitoring system.

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CITE THIS PAPER AS:
Claudia ABINEZA, Valentina E. BALAS, Philibert NSENGIYUMVA, Deep Learning Model for Early Subsequent COPD Exacerbation Prediction, Studies in Informatics and Control, ISSN 1220-1766, vol. 32(3), pp. 99-107, 2023. https://doi.org/10.24846/v32i3y202309