Aortic Risks Prediction Models after Cardiac Surgeries Using Integrated Data
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Journal of Personalized Medicine |
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| Online Access: | https://www.mdpi.com/2075-4426/12/4/637 |
| _version_ | 1797445486495399936 |
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| author | Iuliia Lenivtceva Dmitri Panfilov Georgy Kopanitsa Boris Kozlov |
| author_facet | Iuliia Lenivtceva Dmitri Panfilov Georgy Kopanitsa Boris Kozlov |
| author_sort | Iuliia Lenivtceva |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T13:26:30Z |
| format | Article |
| id | doaj.art-a13cb27d8ded46d3b83cdd5c0c5787fa |
| institution | Directory Open Access Journal |
| issn | 2075-4426 |
| language | English |
| last_indexed | 2024-03-09T13:26:30Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Personalized Medicine |
| spelling | doaj.art-a13cb27d8ded46d3b83cdd5c0c5787fa2023-11-30T21:23:13ZengMDPI AGJournal of Personalized Medicine2075-44262022-04-0112463710.3390/jpm12040637Aortic Risks Prediction Models after Cardiac Surgeries Using Integrated DataIuliia Lenivtceva0Dmitri Panfilov1Georgy Kopanitsa2Boris Kozlov3National Center for Cognitive Research, ITMO University, 49 Kronverskiy Prospect, 197101 Saint-Petersburg, RussiaCardiology Research Institute, Tomsk National Research Medical Center of the Russian Academy of Science, 634012 Tomsk, RussiaNational Center for Cognitive Research, ITMO University, 49 Kronverskiy Prospect, 197101 Saint-Petersburg, RussiaCardiology Research Institute, Tomsk National Research Medical Center of the Russian Academy of Science, 634012 Tomsk, RussiaThe 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.https://www.mdpi.com/2075-4426/12/4/637postoperative risksaortic aneurysmintegrated datapredictive modelingfeature extractionmachine learning |
| spellingShingle | Iuliia Lenivtceva Dmitri Panfilov Georgy Kopanitsa Boris Kozlov Aortic Risks Prediction Models after Cardiac Surgeries Using Integrated Data Journal of Personalized Medicine postoperative risks aortic aneurysm integrated data predictive modeling feature extraction machine learning |
| title | Aortic Risks Prediction Models after Cardiac Surgeries Using Integrated Data |
| title_full | Aortic Risks Prediction Models after Cardiac Surgeries Using Integrated Data |
| title_fullStr | Aortic Risks Prediction Models after Cardiac Surgeries Using Integrated Data |
| title_full_unstemmed | Aortic Risks Prediction Models after Cardiac Surgeries Using Integrated Data |
| title_short | Aortic Risks Prediction Models after Cardiac Surgeries Using Integrated Data |
| title_sort | aortic risks prediction models after cardiac surgeries using integrated data |
| topic | postoperative risks aortic aneurysm integrated data predictive modeling feature extraction machine learning |
| url | https://www.mdpi.com/2075-4426/12/4/637 |
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