01893nam 2200421z- 450 991087939580332120260217142925.0(CKB)5590000001312918(IL-JeEL)995590000001312918(EXLCZ)99559000000131291820240326c2023uuuu -u| |engurcn#nnn|||||txtrdacontentcrdamediacrrdacarrierMultiscale cohort modeling of atrial electrophysiologyrisk stratification for atrial fibrillation through machine learning on electrocardiograms /Claudia NagelKarlsruhe:KIT Scientific Publishing,2023.1 online resourceKarlsruhe transactions on biomedical engineering;273-7315-1281-5 9783731512813 Includes bibliographical references and index.An early detection and diagnosis of atrial fibrillation sets the course for timely intervention to prevent potentially occurring comorbidities. Electrocardiogram data resulting from electrophysiological cohort modeling and simulation can be a valuable data resource for improving automated atrial fibrillation risk stratification with machine learning techniques and thus, reduces the risk of stroke in affected patients.Atrial fibrillationMathematical modelsElectrocardiographyData processingMachine learningBiomedical engineeringAtrial fibrillationMathematical models.ElectrocardiographyData processing.Machine learning.Biomedical engineering616.1Nagel Claudia1893886BOOK9910879395803321Multiscale cohort modeling of atrial electrophysiology4543756UNINA