Machine learning-based risk prediction of intrahospital clinical outcomes in patients undergoing TAVI
Currently, patient selection in TAVI is based upon a multidisciplinary heart team assessment of patient comorbidities and surgical risk stratification. In an era of increasing need for precision medicine and quickly expanding TAVI indications, machine learning has shown promise in making accurate pr...
Gespeichert in:
| Hauptverfasser: | , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2021
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| In: |
Clinical research in cardiology
Year: 2021, Jahrgang: 110, Heft: 3, Pages: 343-356 |
| ISSN: | 1861-0692 |
| DOI: | 10.1007/s00392-020-01691-0 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1007/s00392-020-01691-0
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| Verfasserangaben: | Bruna Gomes, Maximilian Pilz, Christoph Reich, Florian Leuschner, Mathias Konstandin, Hugo A. Katus, Benjamin Meder |
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| 245 | 1 | 0 | |a Machine learning-based risk prediction of intrahospital clinical outcomes in patients undergoing TAVI |c Bruna Gomes, Maximilian Pilz, Christoph Reich, Florian Leuschner, Mathias Konstandin, Hugo A. Katus, Benjamin Meder |
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| 520 | |a Currently, patient selection in TAVI is based upon a multidisciplinary heart team assessment of patient comorbidities and surgical risk stratification. In an era of increasing need for precision medicine and quickly expanding TAVI indications, machine learning has shown promise in making accurate predictions of clinical outcomes. This study aims to predict different intrahospital clinical outcomes in patients undergoing TAVI using a machine learning-based approach. The main clinical outcomes include all-cause mortality, stroke, major vascular complications, paravalvular leakage, and new pacemaker implantations. | ||
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