Machine learning and readmission: Do we need new methods to solve old problems?-Reference-Cited by-同舟云学术

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Machine learning and readmission: Do we need new methods to solve old problems?

Published:2022-01 Issue:1 Volume:163 Page:e101-e102
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:

Nedadur Rashmi,Tam Derrick Y.,Fremes Stephen E.

Publisher

Elsevier BV

Subject

Cardiology and Cardiovascular Medicine,Pulmonary and Respiratory Medicine,Surgery

Reference6 articles.

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

2. Descriptive analysis of 30-day readmission after inpatient surgery discharge in the Veterans Health Administration;Han;JAMA Surg,2014

3. A clinical risk scoring tool to predict readmission after cardiac surgery: an Ontario administrative and clinical population database study;Tam;Can J Cardiol,2018

4. eDoctor: machine learning and the future of medicine;Handelman;J Intern Med,2018

5. How to develop machine learning models for healthcare;Chen;Nat Mater,2019

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

2. REPLY: THE STANDARDIZATION AND AUTOMATION OF MACHINE LEARNING FOR BIOMEDICAL DATA;The Journal of Thoracic and Cardiovascular Surgery;2022-01

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