Using Graphical Models to extract Meaningful, High quality features From Electronic Health Records

I’m focusing on applying some graphical models on Electronic Health Records, freely and openly accessible on the Internet, to extract more robust features that can later be pipelined into well know machine learning algoirhtms. Once these new set of features are confirmed and tested carefully, they may be used for disease prediction (classification tasks),or prediction of other health - related outcomes.

Tensor Factorization for Phenotyping of patients with Chronic Diseases

The purpose of this project was to obtain novel clusters of patients features and attributes using Tensor factorization applied on Electronic Healthcare Records data. Once these clusters or phenotypes are chosen, one can conveniently take adavantage of membership information of each patients and whether they belong to a specific cluster, or a set of attributes, in future machine learning tasks, as summarized, less noisy features. The final outcome being, that once we have more robust and reliable biomedical predictions, we may better be able to deliver appropriate intervention and course of treatment to the patients.