A novel method for Causal Structure Discovery from EHR data, a demonstration on type-2 diabetes mellitus

no code implementations • 11 Nov 2020 • Xinpeng Shen, Sisi Ma, Prashanthi Vemuri, M. Regina Castro, Pedro J. Caraballo, Gyorgy J. Simon

Electronic Health Records (EHR) contain a wealth of real-world data that holds promise for the discovery of disease mechanisms, yet the existing causal structure discovery (CSD) methods fall short on leveraging them due to the special characteristics of the EHR data.

Considerations of automated machine learning in clinical metabolic profiling: Altered homocysteine plasma concentration associated with metformin exposure

no code implementations • 9 Oct 2017 • Alena Orlenko, Jason H. Moore, Patryk Orzechowski, Randal S. Olson, Junmei Cairns, Pedro J. Caraballo, Richard M. Weinshilboum, Liewei Wang, Matthew K. Breitenstein

Automated Machine Learning (AutoML) approaches provide exciting opportunity to guide feature selection in agnostic metabolic profiling endeavors, where potentially thousands of independent data points must be evaluated.

AutoML BIG-bench Machine Learning +1