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Predicting the unpredictable in cardiothoracic surgery.

作者信息

Yadava Om Prakash

机构信息

National Heart Institute, New Delhi, India.

出版信息

Indian J Thorac Cardiovasc Surg. 2023 Mar;39(2):109-111. doi: 10.1007/s12055-023-01478-8. Epub 2023 Jan 26.

Abstract
摘要

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本文引用的文献

1
Machine Learning Methods for Predicting Long-Term Mortality in Patients After Cardiac Surgery.
Front Cardiovasc Med. 2022 May 3;9:831390. doi: 10.3389/fcvm.2022.831390. eCollection 2022.
2
Machine Learning in Cardiac Surgery: Predicting Mortality and Readmission.
ASAIO J. 2022 Dec 1;68(12):1490-1500. doi: 10.1097/MAT.0000000000001696. Epub 2022 May 9.
3
Leveraging Machine Learning to Predict 30-Day Hospital Readmission After Cardiac Surgery.
Ann Thorac Surg. 2022 Dec;114(6):2173-2179. doi: 10.1016/j.athoracsur.2021.11.011. Epub 2021 Dec 8.
5
Supplementing Existing Societal Risk Models for Surgical Aortic Valve Replacement With Machine Learning for Improved Prediction.
J Am Heart Assoc. 2021 Nov 16;10(22):e019697. doi: 10.1161/JAHA.120.019697. Epub 2021 Oct 18.
6
A systematic review of risk prediction models for perioperative mortality after thoracic surgery.
Interact Cardiovasc Thorac Surg. 2021 Apr 8;32(3):333-342. doi: 10.1093/icvts/ivaa273.
7
Improved Prediction by Dynamic Modeling: An Exploratory Study in the Adult Cardiac Surgery Database of the Netherlands Association for Cardio-Thoracic Surgery.
Circ Cardiovasc Qual Outcomes. 2016 Mar;9(2):171-81. doi: 10.1161/CIRCOUTCOMES.114.001645. Epub 2016 Mar 1.

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