| Autor: |
Iuliia Lenivtceva, Dmitri Panfilov, Georgy Kopanitsa, Boris Kozlov |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Journal of Personalized Medicine; Volume 12; Issue 4; Pages: 637 |
| ISSN: |
2075-4426 |
| DOI: |
10.3390/jpm12040637 |
| Popis: |
The complications of thoracic aortic disease include aortic dissection and aneurysm. The risks are frequently compounded by many cardiovascular comorbidities, which makes the process of clinical decision making complicated. The purpose of this study is to develop risk predictive models for patients after thoracic aneurysm surgeries, using integrated data from different medical institutions. Seven risk features were formulated for prediction. The CatBoost classifier performed best and provided an ROC AUC of 0.94–0.98 and an F-score of 0.95–0.98. The obtained results are widely in line with the current literature. The obtained findings provide additional support for clinical decision making, guiding a patient care team prior to surgical treatment, and promoting a safe postoperative period. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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