Electronic Health Records (EHRs) are an emerging data source that enables researchers to employ a data-driven approach for the prediction of health outcomes and for patient risk- stratification. Machine learning methods can be used to identify underlying patterns in an individual’s EHRs, which can predict his/her future health condition. This talk presents two machine learning models, one for identifying patterns of associated medical conditions among patients suffering from kidney disease and the other for identifying CKD severity-stages from standard office visit records.