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

Machine Learning (1501) Clinical Information and Decision Making (1434) Clinical Informatics (1050) Decision Support for Health Professionals (1210) Innovations and Technology in Cancer Care (431) Innovations in Cancer Diagnostic and Decision Support (58)

Published on in Vol 9, No 8 (2021): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29807, first published .
Patient-Level Cancer Prediction Models From a Nationwide Patient Cohort: Model Development and Validation

Patient-Level Cancer Prediction Models From a Nationwide Patient Cohort: Model Development and Validation

Patient-Level Cancer Prediction Models From a Nationwide Patient Cohort: Model Development and Validation

Authors of this article:

Eunsaem Lee1 Author Orcid Image ;   Se Young Jung2 Author Orcid Image ;   Hyung Ju Hwang1 Author Orcid Image ;   Jaewoo Jung3 Author Orcid Image
Article Authors Cited by (10) Tweetations (7) Metrics

Journals

  1. Kennion O, Maitland S, Brady R. Machine learning as a new horizon for colorectal cancer risk prediction? A systematic review. Health Sciences Review 2022;4:100041 View
  2. Gopukumar D, Ghoshal A, Zhao H. Predicting Readmission Charges Billed by Hospitals: Machine Learning Approach. JMIR Medical Informatics 2022;10(8):e37578 View
  3. Loh H, Ooi C, Seoni S, Barua P, Molinari F, Acharya U. Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011–2022). Computer Methods and Programs in Biomedicine 2022;226:107161 View
  4. Afrash M, Shanbehzadeh M, Kazemi-Arpanahi H. Design and Development of an Intelligent System for Predicting 5-Year Survival in Gastric Cancer. Clinical Medicine Insights: Oncology 2022;16 View
  5. Afrash M, Mirbagheri E, Mashoufi M, Kazemi-Arpanahi H. Optimizing prognostic factors of five-year survival in gastric cancer patients using feature selection techniques with machine learning algorithms: a comparative study. BMC Medical Informatics and Decision Making 2023;23(1) View
  6. Isakov O, Riesel D, Leshchinsky M, Shaham G, Reis B, Keret D, Levi Z, Brener B, Balicer R, Dagan N, Hayek S. Development and Validation of a Colorectal Cancer Prediction Model: A Nationwide Cohort-Based Study. Digestive Diseases and Sciences 2024;69(7):2611 View
  7. Peduzzi G, Felici A, Pellungrini R, Campa D. Explainable machine learning identifies a polygenic risk score as a key predictor of pancreatic cancer risk in the UK Biobank. Digestive and Liver Disease 2024 View
  8. Maurer J, Rübner M, Kuo C, Klein B, Franzen J, Wittenborn J, Kupec T, Najjari L, Fasching P, Stickeler E. Random forest algorithm identifies miRNA signatures for breast cancer detection and classification from patient urine samples. Therapeutic Advances in Medical Oncology 2024;16 View

Books/Policy Documents

  1. Sarac F. Explainable Machine Learning for Multimedia Based Healthcare Applications. View