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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.

DOI:10.1007/s12055-023-01478-8
PMID:36785611
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9918619/
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. 2022-5-3

[2]
Machine Learning in Cardiac Surgery: Predicting Mortality and Readmission.

ASAIO J. 2022-12-1

[3]
Leveraging Machine Learning to Predict 30-Day Hospital Readmission After Cardiac Surgery.

Ann Thorac Surg. 2022-12

[4]
Machine learning models for mitral valve replacement: A comparative analysis with the Society of Thoracic Surgeons risk score.

J Card Surg. 2022-1

[5]
Supplementing Existing Societal Risk Models for Surgical Aortic Valve Replacement With Machine Learning for Improved Prediction.

J Am Heart Assoc. 2021-11-16

[6]
A systematic review of risk prediction models for perioperative mortality after thoracic surgery.

Interact Cardiovasc Thorac Surg. 2021-4-8

[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. 2016-3

[8]
Dynamic trends in cardiac surgery: why the logistic EuroSCORE is no longer suitable for contemporary cardiac surgery and implications for future risk models.

Eur J Cardiothorac Surg. 2012-11-14

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