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Unleashing the Power of Very Small Data to Predict Acute Exacerbations of Chronic Obstructive Pulmonary Disease
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- Jacobson, Petra (author)
- Linköpings universitet,Avdelningen för diagnostik och specialistmedicin,Medicinska fakulteten,Region Östergötland, Lungmedicinska kliniken US
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- Lind, Leili (author)
- Linköpings universitet,RISE,Prototypande samhälle,Linköping University, Sweden,Avdelningen för medicinsk teknik,Tekniska fakulteten,RISE Res Inst Sweden, Digital Syst Div, Unit Digital Hlth, Linkoping, Sweden
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- Persson, Lennart (author)
- Linköpings universitet,Avdelningen för diagnostik och specialistmedicin,Medicinska fakulteten,Region Östergötland, Lungmedicinska kliniken US
- (creator_code:org_t)
- Dove Medical Press Ltd, 2023
- 2023
- English.
- In: The International Journal of Chronic Obstructive Pulmonary Disease. - : Dove Medical Press Ltd. - 1176-9106 .- 1178-2005. ; 18, s. 1457-1473
- Journal article (peer-reviewed)
Abstract
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- Introduction: 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. © 2023 Jacobson et al.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Lungmedicin och allergi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Respiratory Medicine and Allergy (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kardiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)
Keyword
- COX proportional hazards
- machine learning
- mHealth
- random forests
- random survival forests
- telehealth or digital health
- Disease Progression
- Humans
- Pulmonary Disease
- Chronic Obstructive
- Sweden
- chronic obstructive lung disease
- disease exacerbation
- human
Publication and Content Type
- ref (subject category)
- art (subject category)
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