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Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Machine Learning Risk Stratification Approach Using Patient-Reported Outcomes for Forecasting Unplanned Health Care

Akina Natori1, Jerry R Bonnell2, Vasileios Stathias3,4

  • 1Division of Medical Oncology, Department of Medicine, University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, Coral Gables, FL.

JCO Clinical Cancer Informatics
|May 26, 2026
PubMed
Summary
This summary is machine-generated.

Integrating patient-reported outcomes with electronic health records improves cancer survivorship risk prediction. Machine learning models using dynamic data windows better forecast health care use and symptom burden, enabling proactive care.

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Area of Science:

  • Oncology
  • Health Informatics
  • Machine Learning

Background:

  • Cancer survivorship care presents challenges in risk stratification due to complex longitudinal data.
  • Multimodal data, including patient-reported outcomes (PROs) and electronic health records (EHRs), are crucial for effective prediction.

Purpose of the Study:

  • To evaluate the added value of integrating PROs with EHR data for predicting adverse cancer survivorship outcomes.
  • To identify optimal temporal windowing strategies for machine learning models in survivorship risk prediction.

Main Methods:

  • A cohort of 25,592 cancer survivors was analyzed over 36 months.
  • Data included baseline measures, treatments, PROs, and healthcare utilization.
  • LASSO and CATBOOST models were applied with various temporal representations (static, cumulative, sliding windows) to predict monthly healthcare utilization and symptom burden.

Main Results:

  • CATBOOST models with time-windowed predictors improved healthcare utilization prediction by 27% (AP = 0.207).
  • PRO integration nearly doubled performance for symptom burden prediction (AP = 0.132 vs. 0.071).
  • Top 10% of high-risk patients captured over 50% of utilization and symptom burden events.

Conclusions:

  • Cancer survivorship risk is dynamic and outcome-specific, requiring tailored prediction approaches.
  • Decoupled, dynamic temporal windows offer a flexible framework for precision-based survivorship care.
  • Integrating PROs and EHRs with advanced machine learning enhances risk stratification.