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Reviews on Innovations in Cancer (106) Digital Health Reviews (3502) Clinical Information and Decision Making (3523) Decision Support for Health Professionals (2113) Innovations and Technology in Cancer Care (662) Machine Learning (3036) Artificial Intelligence (4527)

Published on in Vol 10 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/52322, first published .
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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 (20) 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-Infante L, Marquez G, Parra-Soto S, Cardona-Valencia M, Taramasco C. Machine Learning Techniques Used for the Identification of Sociodemographic Factors Associated With Cancer: Systematic Literature Review. Journal of Medical Internet Research 2026;28:e79187 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
  11. Chi J, Zhong C, Tang J, Guo B, Zhang Y, Deng S, Guo Z, Wu Y. Clinical Decision Support Systems for cancer symptom management: A scoping review. International Journal of Nursing Studies 2026;175:105317 View
  12. Zeinali N, Albashayreh A, Fan W, Gilbertson White S. Using Large Language Models to Detect Anxiety and Nausea/Vomiting Documentation in Clinical Notes of Patients With Cancer. CIN: Computers, Informatics, Nursing 2026;44(4) View
  13. Nicholson B, Sloss E, Smiley A, Finkelstein J, Mooney K. Perception of AI Symptom Models in Oncology Nursing: Mixed Methods Evaluation Study. JMIR Nursing 2026;9:e82283 View
  14. Masood F. AI-enabled psychometric and psychophysiological assessment in psychiatric comorbidity of oncology patients: A systematic review. Journal of Psychosocial Oncology 2026:1 View
  15. Chae S, Bae J, Maitra P, Dunn Lopez K, Sutamtewagul G, Topaz M, Rakel B. Leveraging Natural Language Processing for Symptom Identification in Acute Myeloid Leukemia Using Clinical Notes from Electronic Health Records. Cancer Nursing 2026 View
  16. Hundal J, Veettil A, Chung C, Hosseini M. Artificial Intelligence in Oncology: Practical Applications Across Clinical Care, Scholarship, and Translation. American Society of Clinical Oncology Educational Book 2026;46(3) View
  17. Zhang Y, Wu Y, Wang Y, Yang J, Chen L, Xu C. Perioperative Symptom Trajectories and a Risk Prediction Model for Cervical Cancer: A Prospective Longitudinal Study. International Journal of Women's Health 2026;Volume 18:1 View
  18. Cascella M, Guerra C, De Feo R, Di Lisio F, Giordano P, Esposito W, Cisale G, Cerrone V, Esposito D, Bruno M, Tarallo R, Lombardi M, Troisi J, Galdi M, Martina S, Zarrella A, Rocca E, Filippelli A, Conti V, Montedoro M, Sabbatino F, Polese G, Piazza O. A prospective multimethod investigation of cancer-related pain integrating clinical data and machine learning: results from the RUGGI Study. Journal of Anesthesia, Analgesia and Critical Care 2026;6(1) View

Books/Policy Documents

  1. Bakhshi A, Bakhshi M, Hosseine M, Hasannezhad H, Rahdar A, Fosso‐Kankeu E, Pandey S. Intelligent Nanocarriers. 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