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

Reviews on Innovations in Cancer (52) Clinical Information and Decision Making (1915) Artificial Intelligence (2129) Machine Learning (2076) Innovations and Technology in Cancer Care (531) Digital Health Reviews (1724) Decision Support for Health Professionals (1539)

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 (11) 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
  3. Finkelstein J, Smiley A, Echeverria C, Mooney K. AI-Driven Prediction of Symptom Trajectories in Cancer Care: A Deep Learning Approach for Chemotherapy Management. Bioengineering 2024;11(11):1172 View
  4. Finkelstein J, Smiley A, Echeverria C, Mooney K. Deep Learning Approaches to Forecast Physical and Mental Deterioration During Chemotherapy in Patients with Cancer. Diagnostics 2025;15(8):956 View
  5. Oncu E, Ciftci F. Multimodal AI framework for lung cancer diagnosis: Integrating CNN and ANN models for imaging and clinical data analysis. Computers in Biology and Medicine 2025;193:110488 View
  6. Reunamo A, Moen H, Salanterä S, Lähteenmäki P. Supervised machine learning applied in nursing notes for identifying the need of childhood cancer patients for psychosocial support. Frontiers in Digital Health 2025;7 View
  7. Hou M, Zhu Y, Zhou H, Zhou S, Zhang J, Zhang Y, Liu X. Innovative machine learning approach for liver fibrosis and disease severity evaluation in MAFLD patients using MRI fat content analysis. Clinical and Experimental Medicine 2025;25(1) View
  8. Xu M, Jiang Z, Liao W, Kang Y, Feng X, Jiang K, Jiang Q, Cong Z, Luo J, Wu L, Shen Y, Wang F. Predicting Postoperative Recurrence Using a Support Vector Machine for Patients With Esophageal Squamous Cell Carcinoma: Machine Learning Modeling Development and Validation Study. JMIR Cancer 2025;11:e68027 View
  9. González L, Marquez G, Parra S, Cardona M, Taramasco C. Machine Learning Techniques Used for the Identification of Sociodemographic Factors Associated with Cancer: Systematic Literature Review (Preprint). Journal of Medical Internet Research 2025 View
  10. Bubulac L, Georgescu T, Zivari M, Popescu-Spineni D, Albu C, Bobu A, Nemeth S, Bogdan-Andreescu C, Gurghean A, Alecu A. An Integrative Review of Computational Methods Applied to Biomarkers, Psychological Metrics, and Behavioral Signals for Early Cancer Risk Detection. Bioengineering 2025;12(11):1259 View

Conference Proceedings

  1. Finkelstein J, Smiley A, Huo X, Echeverria C, Mooney K. 2025 IEEE 11th International Conference on Big Data Computing Service and Machine Learning Applications (BigDataService). Hybrid Deep Learning for Early Detection of Symptom Escalation in Patients with Cancer View