Blog

Case Study: Federated Oncology ML, EHR Synthetic Controls & Flu Model

Integrated federated ML, EHR-based synthetic controls, and flu predictive enrollment models are reshaping oncology trials — improving speed, integrity, and access while aligning with recent FDA/EMA guidance.

Federated Learning & Adaptive Randomization for Breast Cancer RWD

Explore how federated learning and adaptive randomization unlock multicenter breast cancer insights, with practical steps, industry views, and advocacy perspectives.

Privacy-Preserving Federated Pipelines & Bayesian Enrollment Forecasts

Practical steps to build privacy-preserving federated pipelines plus Bayesian enrollment forecasts for oncology trials: governance mapping, pilot federated training, Bayesian site-level forecasts, and seasonal operational analytics.

How can federated learning and EHR harmonization boost trial outcomes?

A three-center neuropathy study became a model for how federated learning and EHR harmonization turn messy records into clearer endpoints, faster recruitment, and actionable dashboards that help patients find the trials they need.

4 Practical Tips: EHR-to-CTMS, Survival Modeling, Federated Analytics

Founders and ops teams share war stories — messy EHR extracts, fragile survival models, and federated analytics hurdles. Four practical tips show how to integrate tech, meet regulators, and keep patients at the center.

Boost Oncology & Hypertension Trials: ML, Biomarkers & Real-Time Data Tips

Explore how integrating machine learning for patient stratification, advanced biomarker analytics, and real-time data visualization are revolutionizing oncology and hypertension trials under evolving FDA and EMA guidance.