International Journal of COPD (Jul 2023)
Unleashing the Power of Very Small Data to Predict Acute Exacerbations of Chronic Obstructive Pulmonary Disease
Abstract
Petra Kristina Jacobson,1,2 Leili Lind,3,4 Hans Lennart Persson1,2 1Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden; 2Department of Respiratory Medicine in Linköping, Linköping University, Linköping, Sweden; 3Department of Biomedical Engineering/Health Informatics, Linköping University, Linköping, Sweden; 4Digital Systems Division, Unit Digital Health, RISE Research Institutes of Sweden, Linköping, SwedenCorrespondence: Petra Kristina Jacobson, Department of Respiratory Medicine in Linköping, Linköping University, Linköping, SE-581 85, Sweden, Tel +46 10 1031162, Email [email protected]: In this article, we explore to what extent it is possible to leverage on very small data to build machine learning (ML) models that predict acute exacerbations of chronic obstructive pulmonary disease (AECOPD).Methods: We build ML models using the small data collected during the eHealth Diary telemonitoring study between 2013 and 2017 in Sweden. This data refers to a group of multimorbid patients, namely 18 patients with chronic obstructive pulmonary disease (COPD) as the major reason behind previous hospitalisations. The telemonitoring was supervised by a specialised hospital-based home care (HBHC) unit, which also was responsible for the medical actions needed.Results: We implement two different ML approaches, one based on time-dependent covariates and the other one based on time-independent covariates. We compare the first approach with standard COX Proportional Hazards (CPH). For the second one, we use different proportions of synthetic data to build models and then evaluate the best model against authentic data.Discussion: To the best of our knowledge, the present ML study shows for the first time that the most important variable for an increased risk of future AECOPDs is “maintenance medication changes by HBHC”. This finding is clinically relevant since a sub-optimal maintenance treatment, requiring medication changes, puts the patient in risk for future AECOPDs.Conclusion: The experiments return useful insights about the use of small data for ML.Keywords: machine learning, telehealth or digital health, COX proportional hazards, random survival forests, random forests, mHealth
