Our pioneering work of ML-LHS simulation published by Nature


Our pioneering work on using synthetic patient data to simulate Learning Health Systems (LHS) has been published by Nature. More than a decade after the National Academy of Medicine (NAM) released its vision for Learning Health Systems, there remains a lack of reported examples of machine learning-driven LHS in routine clinical practice. A significant challenge is the dramatic slowdown in research and development speed, as only a small number of researchers can use patient data for machine learning. To accelerate the development of ML-LHS, we synthesized patient data to simulate ML-LHS units for disease risk predictions. This initial study demonstrated that ML-LHS could be continuously improved as more data is accumulated over time from the LHS service. I hypothesize that simulating ML-LHS in the early development phase will expedite the process of building ML-LHS units in clinical settings.


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.