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101 0 $aeng
135   $aurcn#nnn|||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 00$aMultiscale cohort modeling of atrial electrophysiology$erisk stratification for atrial fibrillation through machine learning on electrocardiograms /$fClaudia Nagel
210  1$aKarlsruhe:$cKIT Scientific Publishing,$d2023.
215   $a1 online resource
225 1 $aKarlsruhe transactions on biomedical engineering;$v27
311 08$a3-7315-1281-5 
311 08$a9783731512813 
320   $aIncludes bibliographical references and index.
330   $aAn 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.
606   $aAtrial fibrillation$xMathematical models
606   $aElectrocardiography$xData processing
606   $aMachine learning
606   $aBiomedical engineering
615  0$aAtrial fibrillation$xMathematical models.
615  0$aElectrocardiography$xData processing.
615  0$aMachine learning.
615  0$aBiomedical engineering
676   $a616.1
700   $aNagel$b Claudia$01893886
906   $aBOOK
912   $a9910879395803321
996   $aMultiscale cohort modeling of atrial electrophysiology$94543756
997   $aUNINA