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July 20, 2026IISE Transactions on Healthcare Systems Engineering

Multi-modal machine learning for breast cancer recurrence prediction

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Authors

JSJiahao ShaoXWXudong WangAKAnam Nawaz Khan

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Overview

Randomized trial shows improved prediction accuracy in breast cancer recurrence using multi-modal data integration, suggesting enhanced follow-up care.

Key Points

  • This study aims to improve breast cancer recurrence prediction by integrating various clinical data sources.
  • Integrating multi-modal clinical data including treatment records, pathology reports, and clinician notes.
  • Utilizing a rule-based extraction mechanism and conflict reconciliation strategy to enhance data accuracy.
  • Benchmarking performance against established feature sets from previous studies.
  • Multi-modal integration leads to a significant improvement in predictive accuracy over single-modal methods.
  • The accuracy enhancement is evident across various machine learning models evaluated.

Cite This Study

Shao et al. (2026) studied this question.

synapsesocial.com/papers/6a5dba128bd453d3397ab541https://doi.org/10.1080/24725579.2026.2701461
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Predicting disease recurrence in breast cancer patients using machine learning models with clinical and radiomic characteristics: a retrospective study2024 · 7 citations
  2. 2Multi-Scale MRI, Radiology Reports, and Blood Biomarker–Guided Multimodal Deep Learning for Predicting Postoperative Recurrence in Cervical Cancer: A Multicenter Study2026
  3. 3Multimodal Machine Learning-Based Cancer Progression Prediction from Plain Radiographs and Clinical Data2025
  4. 4Multimodal machine learning integrates clinical and comorbidity data to predict breast cancer prognosis and treatment outcomes2026
  5. 5Multimodal Machine Learning for Early Prediction of Metastasis in a Swedish Multicancer Cohort2026