Researchers developed AI models using EHR and patient-reported outcomes to identify cancer survivors at high risk for emergency visits and worsening symptoms.
Key Details
- 1Study by Sylvester Comprehensive Cancer Center (University of Miami) published in JCO Clinical Cancer Informatics (May 2026).
- 2AI models analyzed data from over 25,000 cancer survivors followed for three years.
- 3Combined EHR and PRO data to predict unplanned healthcare use (ED visits, hospitalizations) and elevated symptom burden.
- 4Recent clinical activity helped predict acute events, while long-term PRO trends improved symptom prediction.
- 5Adding PROs nearly doubled symptom forecasting accuracy compared to using clinical data alone.
- 6Top 10% of risk-flagged patients accounted for half of downstream healthcare events and symptom episodes.
Why It Matters
Integrating AI-based risk stratification into survivorship care could enable earlier, more targeted interventions for cancer survivors. This demonstrates the evolving potential of medical AI to support proactive patient management across radiology-adjacent fields.
Source
EurekAlert
Related News
•EurekAlert
NYU AI Model Improves 5-Year Breast Cancer Risk Prediction via 3D Mammograms
NYU researchers developed an AI model using longitudinal 3D mammograms that outperforms single-scan and 2D-based tools in predicting 5-year breast cancer risk.
•EurekAlert
AI Framework Accelerates Aortic Aneurysm Risk Prediction from Imaging
Researchers developed BioPINN-LM, combining physics-informed neural networks and multimodal large language models to deliver fast, interpretable risk assessments for ascending thoracic aortic aneurysms.
•EurekAlert
Study Finds Patient Voices Missing in Generative AI Design for Oncology
A Flinders University-led review found patients and carers are rarely involved in shaping generative AI tools used in oncology.