title: Longitudinal data driven study design.
abstract: In this paper we present a method for processing EHR data into a longitudinal data model and examples for using this model to identify patients cross-sectionally and longitudinally as well as testing study designs retrospectively. Our data model describes measurements on four dimensions: the associated patient, observed feature, data source and time of survey. The transformation of structured source data into our model is defined by rules written in XML. To showcase the flexibility of the proposed longitudinal data model, we present an evolution of a retrospective study design as well as an example for interpreting biomarkers in emergency situations. With the proposed longitudinal data model complex queries can be performed, study designs tested and optimized.
authors.affiliation: Department of Cardiology, University Hospital Heidelberg, Heidelberg, Germany.
authors.forename: Hauke
authors.initials: H
authors.lastname: Hund
authors.affiliation: GECKO Research Institute, Heilbronn University, Heilbronn, Germany.
authors.forename: Sven
authors.initials: S
authors.lastname: Gerth
authors.affiliation: Department of Cardiology, University Hospital Heidelberg, Heidelberg, Germany.
authors.forename: Dirk
authors.initials: D
authors.lastname: Lossnitzer
authors.affiliation: GECKO Research Institute, Heilbronn University, Heilbronn, Germany.
authors.forename: Christian
authors.initials: C
authors.lastname: Fegeler
mesh_terms: D003198:Computer Simulation; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D057286:Electronic Health Records / Q000458:organization & administration* / Q000706:statistics & numerical data*; D006331:Heart Diseases / Q000175:diagnosis*; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D018570:Risk Assessment / Q000379:methods*