Book
Development of a Machine Learning Model for Screening Sleep Apnea in Heart Failure Patients Using Sleep Sensor Data
| Τίτλος: | Development of a Machine Learning Model for Screening Sleep Apnea in Heart Failure Patients Using Sleep Sensor Data |
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| Συγγραφείς: | Gunasegarama, Mathushan, Dinesen, Birthe, Müller Larsen, Nikolaj, Ghamari Gilavai, Ghazal, Røge, Kristine, Kirk Østergaard, Mathias, Rovsing Jochumsen, Mads |
| Πηγή: | Gunasegarama, M, Dinesen, B, Müller Larsen, N, Ghamari Gilavai, G, Røge, K, Kirk Østergaard, M & Rovsing Jochumsen, M 2025, Development of a Machine Learning Model for Screening Sleep Apnea in Heart Failure Patients Using Sleep Sensor Data. in Good Evaluation-Better Digital Health. IOS Press, Studies in Health Technology and Informatics, vol. 332, pp. 62-66, EFMI Special Topic Conference 2025, Osnabrück, Germany, 20/10/2025. https://doi.org/10.3233/SHTI251496 |
| Στοιχεία εκδότη: | IOS Press, 2025. |
| Έτος έκδοσης: | 2025 |
| Θεματικοί όροι: | Machine Learning, Male, Sleep Apnea Syndromes/diagnosis, Diagnosis, Computer-Assisted/methods, Heart Failure/complications, Mass Screening/methods, Humans, Reproducibility of Results, Polysomnography/methods, Female, Sensitivity and Specificity |
| Περιγραφή: | Sleep apnea (SA) is a prevalent disorder among individuals with heart failure (HF), often leading to complications. Early identification is essential for timely interventions and better outcomes. This study explores the feasibility of developing a screening tool for SA in patients with HF using data from the Future Patient Telerehabilitation program. A random forest classifier was used to develop a predictive model, achieving a promising receiver operating characteristic area under the curve (ROC-AUC) of 0.85, suggesting that the random forest classifier has the potential as a SA screening tool for HF patients. However, the study lacked key variables, such as oxygen saturation, that are strong predictors for SA assessment according to current literature; this limits the model’s generalizability. Despite this, the findings indicate that the ML model shows promise for screening SA in HF patients, highlighting the need for high-quality, standardized data from future clinical trials to enhance its accuracy and clinical utility. |
| Τύπος εγγράφου: | Part of book or chapter of book |
| Περιγραφή αρχείου: | application/pdf |
| ISSN: | 1879-8365 0926-9630 |
| DOI: | 10.3233/shti251496 |
| Σύνδεσμος πρόσβασης: | https://vbn.aau.dk/da/publications/d6c0a653-d6c5-4b54-b6ba-c1ca86fcc710 https://vbn.aau.dk/ws/files/798726352/SHTI-332-SHTI251496.pdf https://doi.org/10.3233/SHTI251496 |
| Αριθμός Καταχώρησης: | edsair.dedup.wf.002..8c6edb253ca1d15c2583c7124bbafa43 |
| Βάση Δεδομένων: | OpenAIRE |
| ISSN: | 18798365 09269630 |
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| DOI: | 10.3233/shti251496 |