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

RCTs - Protocols/Proposals (eHealth) (668) Innovations and Technology in Cancer Care (531) Machine Learning (2074) Decision Support for Health Professionals (1538) Breast Cancer (290) Web-based and Mobile Health Interventions (4017)

Published on in Vol 12 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48852, first published .
A Machine Learning Model to Predict Patients’ Adherence Behavior and a Decision Support System for Patients With Metastatic Breast Cancer: Protocol for a Randomized Controlled Trial

A Machine Learning Model to Predict Patients’ Adherence Behavior and a Decision Support System for Patients With Metastatic Breast Cancer: Protocol for a Randomized Controlled Trial

A Machine Learning Model to Predict Patients’ Adherence Behavior and a Decision Support System for Patients With Metastatic Breast Cancer: Protocol for a Randomized Controlled Trial

Authors of this article:

Marianna Masiero1, 2 Author Orcid Image ;   Gea Elena Spada2 Author Orcid Image ;   Virginia Sanchini1 Author Orcid Image ;   Elisabetta Munzone3 Author Orcid Image ;   Ricardo Pietrobon4 Author Orcid Image ;   Lucas Teixeira4 Author Orcid Image ;   Mirtha Valencia4 Author Orcid Image ;   Aline Machiavelli4 Author Orcid Image ;   Elisa Fragale2 Author Orcid Image ;   Massimo Pezzolato1, 2 Author Orcid Image ;   Gabriella Pravettoni1, 2 Author Orcid Image
Article Authors Cited by (9) Tweetations (2) Metrics

Journals

  1. Masiero M, Spada G, Fragale E, Pezzolato M, Munzone E, Sanchini V, Pietrobon R, Teixeira L, Valencia M, Machiavelli A, Woloski R, Marzorati C, Pravettoni G. Adherence to oral anticancer treatments: network and sentiment analysis exploring perceived internal and external determinants in patients with metastatic breast cancer. Supportive Care in Cancer 2024;32(7) View
  2. Marzorati C, Masiero M, Pravettoni G. “Perspective: An integrated vision of the quality of life in breast cancer survivorship trajectory”. The Breast 2024;77:103785 View
  3. Haga S. Artificial intelligence, medications, pharmacogenomics, and ethics. Pharmacogenomics 2024;25(14-15):611 View
  4. Chen D, He E, Pace K, Chekay M, Raman S. Concordance with SPIRIT-AI guidelines in reporting of randomized controlled trial protocols investigating artificial intelligence in oncology: a systematic review. The Oncologist 2025;30(5) View
  5. Froicu E, Creangă-Murariu I, Afrăsânie V, Gafton B, Alexa-Stratulat T, Miron L, Pușcașu D, Poroch V, Bacoanu G, Radu I, Marinca M. Artificial Intelligence and Decision-Making in Oncology: A Review of Ethical, Legal, and Informed Consent Challenges. Current Oncology Reports 2025;27(8):1002 View
  6. Chair S, Huang Z, Fan M, Baig M, Gao R. The Effect of Theory‐Guided Positive Psychological Interventions for Patients With Breast Cancer: A Systematic Review and Meta‐Analysis. Journal of Clinical Nursing 2026;35(1):33 View
  7. Aapro M, Waldrop J, Ciani O, Drury A, Wiseman T, Masiero M, Matuszewska J, Paluch-Shimon S, Pravettoni G, Henze F, Wuerstlein R, Zambon M, Simón Robleda S, Presti P, Fenderico N. Collaborative Funding Model to Improve Quality of Care for Metastatic Breast Cancer in Europe. Current Oncology 2025;32(10):547 View
  8. Masiero M, Marzorati C, Fragale E, Munzone E, Pietrobon R, Teixeira L, Machiavelli A, Pravettoni G. Optimistic Bias and Personality Factors in Adherence to Oral Anticancer Treatments in Metastatic Breast Cancer: A Retrospective Analysis of Data From a Randomized Controlled Trial. Cancer Control 2025;32 View
  9. Durosini I, Masiero M, Strika M, Mazzoni D, Pravettoni G. Attitudes toward the use of ChatGPT to seek oncological information: a qualitative study. Frontiers in Psychology 2025;16 View