Moffitt Cancer Center researchers created machine learning models that use patient-reported outcomes and wearable data to predict urgent care visits for non-small cell lung cancer patients.
Key Details
- 1Machine learning models incorporated wearable sensor data (Fitbit) and quality-of-life surveys from 58 non–small cell lung cancer patients.
- 2Models using patient-reported and wearable data outperformed those using only clinical/demographic data in predicting urgent care visits during systemic therapy.
- 3Researchers employed explainable Bayesian Networks, revealing how symptom, sleep, and lab data affect risk.
- 4Study highlights potential to proactively intervene and prevent hospitalizations due to treatment complications.
- 5This was a single-center study with a modest sample; larger validation is planned.
Why It Matters
Integrating diverse patient-originated health data with explainable AI models could transform personalized cancer care by allowing real-time identification of high-risk patients and early intervention, potentially preventing costly or harmful acute events. Such approaches expand the role of AI in predictive analytics for oncology.
Source
EurekAlert
Related News
•EurekAlert
Researchers Develop All-Optical Synapse for Neuromorphic Imaging Systems
A new artificial synapse, controlled entirely by light, enables in-sensor neuromorphic processing for more efficient and noise-resistant imaging systems.
•EurekAlert
Mayo Clinic Showcases Imaging AI and Early Cancer Detection Advances at ASCO 2026
Mayo Clinic researchers will present over 30 studies at ASCO 2026, highlighting new advances in imaging AI, data science, and early cancer detection.
•EurekAlert
AI-Simulation Approach Achieves 90% Faster Brain MRI with Minimal Data
A simulation-based AI method can reconstruct brain MRI scans with only 10% of the usual data, greatly reducing scan times.