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ELHS Institute’s Pioneering Work on ML-LHS Simulation Published in Nature Scientific Reports

 

📅 October 26, 2022

 

The First Simulated ML-LHS Unit 

 

We are proud to announce that our groundbreaking research on simulating Learning Health System (LHS) units using synthetic patient data has been published by Nature.

 

More than a decade after the National Academy of Medicine (NAM) introduced its vision for Learning Health Systems, real-world implementation of machine learning-driven LHS (ML-LHS) in routine clinical practice remains scarce. One major barrier is the limited access to patient data, which significantly slows research and development.

 

To accelerate ML-LHS development, we pioneered the use of synthetic patient data to simulate ML-LHS units for disease risk prediction. This study demonstrated that ML-LHS can continuously improve as more real-world data accumulates over time.

 

We hypothesize that simulating ML-LHS during early development will expedite the deployment of ML-LHS units in clinical settings, making AI-driven learning health systems a reality sooner. 🚀

 

Reference:

Chen A, Chen DO. Simulation of a machine learning enabled learning health system for risk prediction using synthetic patient data. Nature Sci Rep. 2022;12(1):17917. doi:10.1038/s41598-022-23011-4.