REPLY: THE STANDARDIZATION AND AUTOMATION OF MACHINE LEARNING FOR BIOMEDICAL DATA-Reference-Cited by-同舟云学术

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REPLY: THE STANDARDIZATION AND AUTOMATION OF MACHINE LEARNING FOR BIOMEDICAL DATA

Published:2022-01 Issue:1 Volume:163 Page:e102-e103
ISSN:0022-5223
Container-title:The Journal of Thoracic and Cardiovascular Surgery
language:en
Short-container-title:The Journal of Thoracic and Cardiovascular Surgery

Author:

Ishwaran Hemant,O'Brien Robert

Funder

National Institute of General Medical Sciences

Publisher

Elsevier BV

Subject

Cardiology and Cardiovascular Medicine,Pulmonary and Respiratory Medicine,Surgery

Reference9 articles.

1. Using machine learning to predict early readmission following esophagectomy;Bolourani;J Thorac Cardiovasc Surg,2021

2. Machine learning and readmission: do we need new methods to solve old problems?;Nedadur;J Thorac Cardiovasc Surg,2022

3. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets;Saito;PLoS One,2015

4. Precision-recall-gain curves: PR analysis done right;Flach,2015

5. Disadvantages of using the area under the receiver operating characteristic curve to assess imaging tests: a discussion and proposal for an alternative approach;Halligan;Eur Radiol,2015

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Application of machine learning methods to predict progression in patients with hormone-sensitive prostate cancer;2023-09-06

2. Reply: In machine learning, the devil is in the details;The Journal of Thoracic and Cardiovascular Surgery;2022-01

3. Limitations of receiver operating characteristic curve on imbalanced data: Assist device mortality risk scores;The Journal of Thoracic and Cardiovascular Surgery;2021-07

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