title: Detecting Duplicates at Hospital Admission: Comparison of Deterministic and Probabilistic Record Linkage.
abstract: Effective detection of corresponding or duplicate records in medical data sets is vital for a high quality health care system. We evaluate the efficacy of several current and novel record linkage approaches by modeling a hospital-admission scenario, wherein an incoming patient may or may not have been previously treated. Our work is to develop recommendations for how an automated system could operate in such a scenario, especially regarding comparison and classification. By using a large, anonymous, real-world data set, we can gain insight into the robustness of these methods in a way that artificial data sets cannot provide. Preliminary results show that even minor confounders have deleterious effects on our ability to classify matches. We aim to evaluate and refine a semi-supervised classification technique to cope with these influences.
authors.affiliation: Institute for Medical Informatics, Biometry and Epidemiology, Faculty of Medicine, University Duisburg-Essen, Essen, Germany.
authors.forename: Andreas
authors.initials: A
authors.lastname: Waldenburger
authors.affiliation: Institut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie, Ludwig-Maximilians-Universität München, Germany.
authors.forename: Daniel
authors.initials: D
authors.lastname: Nasseh
authors.affiliation: Institute for Medical Informatics, Biometry and Epidemiology, Faculty of Medicine, University Duisburg-Essen, Essen, Germany.
authors.forename: Jürgen
authors.initials: J
authors.lastname: Stausberg
mesh_terms: D015986:Confounding Factors, Epidemiologic; D006785:Hospitals, University; D006801:Humans; D008498:Medical Record Linkage / Q000379:methods*; D010343:Patient Admission