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

Machine Learning (1501) Artificial Intelligence (1421) Clinical Information and Decision Making (1434) Decision Support for Health Professionals (1210) Innovations and Technology in Cancer Care (431)

Published on in Vol 9, No 3 (2021): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25635, first published .
Machine Learning Approach to Predict the Probability of Recurrence of Renal Cell Carcinoma After Surgery: Prediction Model Development Study

Machine Learning Approach to Predict the Probability of Recurrence of Renal Cell Carcinoma After Surgery: Prediction Model Development Study

Machine Learning Approach to Predict the Probability of Recurrence of Renal Cell Carcinoma After Surgery: Prediction Model Development Study

Authors of this article:

HyungMin Kim1, 2 Author Orcid Image ;   Sun Jung Lee1, 2 Author Orcid Image ;   So Jin Park1, 2 Author Orcid Image ;   In Young Choi1, 2 Author Orcid Image ;   Sung-Hoo Hong3 Author Orcid Image
Article Authors Cited by (13) Tweetations (4) Metrics

HyungMin Kim   1, 2 , MSc ;   Sun Jung Lee   1, 2 , BSc ;   So Jin Park   1, 2 , MSc ;   In Young Choi   1, 2 * , PhD ;   Sung-Hoo Hong   3 * , MD, PhD

1 Department of Medical Informatics, College of Medicine, The Catholic University, Seoul, Republic of Korea

2 Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University, Seoul, Republic of Korea

3 Department of Urology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University, Seoul, Republic of Korea

*these authors contributed equally

Corresponding Author:

  • Sung-Hoo Hong, MD, PhD
  • Department of Urology
  • Seoul St. Mary’s Hospital
  • College of Medicine, The Catholic University
  • 222, Banpo-daero, Seocho-gu
  • Seoul
  • Republic of Korea
  • Phone: 82 2-2258-6228
  • Email: toomey@catholic.ac.kr