Predicting Missing Clinical Follow-Ups with AI-Generated Synthetic Data

In secondary cardiovascular prevention, missing or not-yet-observed clinical follow-ups can delay evidence generation and patient care decisions. In this use case, Aindo partnered with a global biopharmaceutical company to address this challenge using its Europrivacy-certified generative AI technology.

Starting from real-world data from 700 patients, Aindo’s model generated statistically plausible follow-up laboratory measurements based on each patient’s baseline profile and treatment intensity. These values were then used to predict whether patients would reach their LDL-C targets. The model correctly distinguished between patients who reached their target and those who did not in approximately three out of four cases, consistently outperforming a leading machine-learning benchmark across repeated validation tests.

Download the full use case to discover how synthetic data accelerates clinical research timelines, boosts predictive performance, and unlocks actionable real-world evidence safely.