Forecasting Risk and Redefining Cancer Survivorship Care

Forecasting Risk and Redefining
Cancer Survivorship Care

For a growing number of cancer survivors, ringing the bell at the end of primary treatment marks a transition into a complex phase of care that is often less structured and harder to predict. Even after therapy concludes, patients may experience lingering physical symptoms, emotional distress, or other unexpected medical needs that lead to emergency […]

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Digital Health + Life Sciences Informatics, Fall 2026, Publications

by Monica Schuloff Smith

A new multidisciplinary study from Sylvester Comprehensive Cancer Center, part of the University of Miami Miller School of Medicine, suggests that the key to anticipating those outcomes may lie in closely examining electronic health records and patient-reported data systematically, using novel artificial intelligence (AI) technologies.

Read the full article “Redefining Cancer Survivorship Care: How AI Technology and Big Data are Contributing to Proactive Care Delivery” at Inventum.

Congratulations to IDSC Authors Ravi Vadapalli, Jerry Bonnell, and Mitsunori Ogihara, and their co-authors led by Akina Natori, M.D., M.S.P.H., a Sylvester oncologist and assistant professor in the Division of Medical Oncology at the Miller School, on the publication of “Machine Learning Risk Stratification Approach Using Patient-Reported Outcomes for Forecasting Unplanned Health Care Use and Symptom Burden in Cancer Survivors” in the JCO Clincial Cancer Informatics Journal.

Ravi Vadapalli, Jerry Bonnell, Mistu Ogihara

Natori Akina

Abstract

Purpose

Effective risk stratification in cancer survivorship requires handling longitudinal data characterized by multimodal inputs, irregular follow-up, and recurrent clinical events. This study evaluated the incremental value of integrating patient-reported outcomes (PROs) with electronic health record (EHR) data and identified optimal windowing strategies for machine learning–based prediction of adverse survivorship outcomes.

Patients and Methods

This study used a cohort of 25,592 cancer survivors followed for 36 months. Data from four domains were integrated: baseline measures, treatments, PROs, and health care utilization (emergency room visits and hospitalizations). Two classification models, LASSO and CATBOOST, were applied across modality combinations and five temporal representations of patient history: static early-phase (0-6 months), cumulative history, sliding windows (4- and 12-month), and a most-recent baseline. Performance was evaluated for predicting monthly health care utilization and patient-reported symptom burden using average precision (AP). SHapley Additive exPlanations (SHAP) analysis identified key predictors and characterized their evolving influence.

Results

For health care utilization, CATBOOST models trained on the full multimodal data set with time-windowed predictors achieved strong discrimination (AP = 0.207), outperforming static baselines by 27%. SHAP analyses emphasized dynamic contributions from recent utilization and treatment toxicity. For symptom burden, PRO integration was crucial, nearly doubling clinical-only performance (AP = 0.132 v 0.071), with longer historical context improving characterization of progressive functional decline and symptom severity. Flagging the top 10% of patients by predicted risk captured 51.7% of health care utilizations and 46.7% of symptom burden events.

Conclusion

Adverse survivorship risk is dynamic and outcome-specific: acute health care utilization is best predicted by recent clinical momentum, while longitudinal patient-reported trends drive symptom burden. Implementing decoupled, dynamic windows provides a flexible framework for risk stratification and risk prediction beyond standard clinical heuristics, facilitating proactive, precision-based survivorship care.

Read the full paper at ascopubs.org.

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Akina Natori, Jerry R. Bonnell, Vasileios Stathias, Sara E. Fleszar-Pavlovic, Mitsunori Ogihara, Andrew Wang, Ravi Vadapalli, Blanca Silvia Noriega Esquives, Tracy E. Crane, Frank J. Penedo. Machine Learning Risk Stratification Approach Using Patient-Reported Outcomes for Forecasting Unplanned Health Care Use and Symptom Burden in Cancer Survivors. JCO Clin Cancer Inform 10, e2500389(2026). DOI: 10.1200/CCI-25-00389

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