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

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;16:117955492211168 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 View

Books/Policy Documents

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