{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T08:58:27Z","timestamp":1761901107745,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643686295"}],"license":[{"start":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T00:00:00Z","timestamp":1759363200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,2]]},"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3233\/shti251496","type":"book-chapter","created":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T09:57:06Z","timestamp":1759485426000},"source":"Crossref","is-referenced-by-count":0,"title":["Development of a Machine Learning Model for Screening Sleep Apnea in Heart Failure Patients Using Sleep Sensor Data"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-8041-9508","authenticated-orcid":false,"given":"Mathushan","family":"Gunasegaram","sequence":"first","affiliation":[{"name":"Laboratory Welfare Technologies \u2013 Digital Health & Rehabilitation, ExerciseTech, Department of Health Science and Technology, Aalborg University (AAU), Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Birthe","family":"Dinesen","sequence":"additional","affiliation":[{"name":"Laboratory Welfare Technologies \u2013 Digital Health & Rehabilitation, ExerciseTech, Department of Health Science and Technology, Aalborg University (AAU), Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nikolaj","family":"M\u00fcller Larsen","sequence":"additional","affiliation":[{"name":"Laboratory Welfare Technologies \u2013 Digital Health & Rehabilitation, ExerciseTech, Department of Health Science and Technology, Aalborg University (AAU), Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ghazal","family":"Ghamari Gilavai","sequence":"additional","affiliation":[{"name":"Laboratory Welfare Technologies \u2013 Digital Health & Rehabilitation, ExerciseTech, Department of Health Science and Technology, Aalborg University (AAU), Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kristine","family":"R\u00f8ge","sequence":"additional","affiliation":[{"name":"Laboratory Welfare Technologies \u2013 Digital Health & Rehabilitation, ExerciseTech, Department of Health Science and Technology, Aalborg University (AAU), Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mathias","family":"Kirk \u00d8stergaard","sequence":"additional","affiliation":[{"name":"Laboratory Welfare Technologies \u2013 Digital Health & Rehabilitation, ExerciseTech, Department of Health Science and Technology, Aalborg University (AAU), Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mads","family":"Rovsing Jochumsen","sequence":"additional","affiliation":[{"name":"Neural Engineering and Neurophysiology, Department of Health Science and Technology, AAU, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Good Evaluation - Better Digital Health"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI251496","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T08:38:10Z","timestamp":1761899890000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI251496"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,2]]},"ISBN":["9781643686295"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti251496","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2025,10,2]]}}}