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

Reviews on Innovations in Cancer (31) Clinical Information and Decision Making (1407) Artificial Intelligence (1371) Machine Learning (1465) Innovations and Technology in Cancer Care (423) Digital Health Reviews (1289) Decision Support for Health Professionals (1185)

Published on in Vol 10 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/52322, first published .
Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic Review

Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic Review

Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic Review

Authors of this article:

Nahid Zeinali1 Author Orcid Image ;   Nayung Youn2 Author Orcid Image ;   Alaa Albashayreh2 Author Orcid Image ;   Weiguo Fan3 Author Orcid Image ;   Stéphanie Gilbertson White2 Author Orcid Image
Article Authors Cited by (2) Tweetations (2) Metrics

Journals

  1. Valencia-Muntalà L, Gómez-Vaquero C, Berbel-Arcobé L, Benavent D, Vidal-Montal P, Juanola X, Narváez J, Nolla J. Assessing fatigue in women over 50 years with rheumatoid arthritis: a comprehensive case-control study using the FACIT-F scale. Frontiers in Medicine 2024;11 View
  2. Bandyopadhyay A, Albashayreh A, Zeinali N, Fan W, Gilbertson-White S. Using real-world electronic health record data to predict the development of 12 cancer-related symptoms in the context of multimorbidity. JAMIA Open 2024;7(3) View