Abstract: Sickle cell disease (SCD) is a heterogeneous chronic disease with a range of disease phenotypes defined by the frequency, severity and cumulative effect of known disease-associated adverse events. The present work seeks to leverage the abundant data on SCD patients that are available within Electronic Health Records (EHRs) to develop computable phenotypes that accurately represent features of SCD etiology and progression.
Learning Objective 1: Describe approaches to discovery of patient care patterns in EHR using machine learning techniques.
H Timothy Bunnell, Nemours Alfred I duPont Hospital for Children
Erin Crowgey (Presenter)
Nemours Alfred I duPont Hospital for Children
Anders Kolb, Nemours Alfred I duPont Hospital for Children