This paper is in the following e-collection/theme issue:

Machine Learning (1373) Clinical Information and Decision Making (1315) Decision Support for Health Professionals (1129) Clinical Cancer Research (71)

Published on in Vol 9, No 7 (2021): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27110, first published .
Prediction Model of Anastomotic Leakage Among Esophageal Cancer Patients After Receiving an Esophagectomy: Machine Learning Approach

Prediction Model of Anastomotic Leakage Among Esophageal Cancer Patients After Receiving an Esophagectomy: Machine Learning Approach

Prediction Model of Anastomotic Leakage Among Esophageal Cancer Patients After Receiving an Esophagectomy: Machine Learning Approach

Authors of this article:

Ziran Zhao1 Author Orcid Image ;   Xi Cheng2 Author Orcid Image ;   Xiao Sun3 Author Orcid Image ;   Shanrui Ma1 Author Orcid Image ;   Hao Feng1 Author Orcid Image ;   Liang Zhao1 Author Orcid Image
Article Authors Cited by (5) Tweetations (3) Metrics

Journals

  1. Klein S, Duda D. Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas. Cancers 2021;13(19):4919 View
  2. Alharbe N, Munshi R, Khayyat M, Khayyat M, Abdalaha Hamza S, Aljohani A, Lydia L. Atom Search Optimization with the Deep Transfer Learning-Driven Esophageal Cancer Classification Model. Computational Intelligence and Neuroscience 2022;2022:1 View
  3. Jung J, Pisula J, Bozek K, Popp F, Fuchs H, Schröder W, Bruns C, Schmidt T. Prediction of postoperative complications after oesophagectomy using machine-learning methods. British Journal of Surgery 2023;110(10):1361 View
  4. Zhang C, Jia D, Li Z, Wu N, He Z, Jiang H, Yan Q. Esophageal cancer detection framework based on time series information from smear images. Expert Systems with Applications 2024;238:122362 View
  5. Nopour R. Design of risk prediction model for esophageal cancer based on machine learning approach. Heliyon 2024;10(2):e24797 View