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Laqua, Fabian Christopher
[Verfasser:in]
;
Dörr, Marcus
[Akademische:r Betreuer:in];
Baeßler, Bettina
[Akademische:r Betreuer:in]
Universität Greifswald
Deep learning prediction of all-cause-mortality in a general population cohort by myocardial strain derived from speckle-tracking-echocardiography
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- Medientyp: E-Book; Hochschulschrift
- Titel: Deep learning prediction of all-cause-mortality in a general population cohort by myocardial strain derived from speckle-tracking-echocardiography
- Beteiligte: Laqua, Fabian Christopher [Verfasser:in]; Dörr, Marcus [Akademische:r Betreuer:in]; Baeßler, Bettina [Akademische:r Betreuer:in]
- Körperschaft: Universität Greifswald
-
Erschienen:
Greifswald, 2022
- Umfang: 1 Online-Ressource (PDF-Datei: 85 Seiten, 2167 Kilobyte); Diagramme (teilweise farbig)
- Sprache: Englisch
- Identifikator:
-
RVK-Notation:
ST 300 : Allgemeines
YB 8504 : Dissertation, Habilitationsarbeit
YB 9004 : Dissertation, Habilitationsarbeit
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Schlagwörter:
Bevölkerung
>
Herzkrankheit
>
Herzmuskel
>
Sterblichkeit
>
Überlebenszeit
>
Ultraschallkardiografie
>
Deep learning
>
Prognose
- Entstehung:
-
Hochschulschrift:
Dissertation, Universitätsmedizin der Universität Greifswald, 2023
-
Anmerkungen:
Literaturverzeichnis: Seite 43-50
-
Beschreibung:
Überlebenszeit , Prognose , Ultraschallkardiografie, Deep Learning, Echocardiography, Survival, general population, prediction, speckle-tracking
Background Previous work has focused on speckle-tracking echocardiography (STE)-derived global longitudinal and circumferential peak strain as potential superior prognostic metric markers compared with left ventricular ejection fraction (LVEF). However, the value of regional distribution and the respective orientation of left ventricular wall motion (quantified as strain and derived from STE) for survival prediction have not been investigated yet. Moreover, most of the recent studies on risk stratification in primary and secondary prevention do not use neural networks for outcome prediction. Purpose To evaluate the performance of neural networks for predicting all cause-mortality with different model inputs in a moderate-sized general population cohort. Methods All participants of the second cohort of the population-based Study of Health in Pomerania (SHIP-TREND-0) without prior cardiovascular disease (CVD; acute myocardial infarction, cardiac surgery/intervention, heart failure and stroke) and with transthoracic echocardiography exams were followed for all-cause mortality from baseline examination (2008-2012) until 2019. A novel deep neural network architecture ‘nnet-Surv-rcsplines’, that extends the Royston-Parmar- cubic splines survival model to neural networks was proposed and applied to predict all-cause mortality from STE-derived global and/or regional myocardial longitudinal, circumferential, transverse, and radial strain in addition to the components of the ESC SCORE ... - Zugangsstatus: Freier Zugang