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.
Dokumententyp: | Konferenzbeitrag (Bericht) |
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Fakultät: | Medizin > Institut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie |
Themengebiete: | 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit |
ISBN: | 978-1-61499-663-7 ; 978-1-61499-664-4 |
Ort: | Amsterdam, [Netherlands] ; Berlin, [Germany] ; Washington, District of Columbia |
Sprache: | Englisch |
Dokumenten ID: | 36682 |
Datum der Veröffentlichung auf Open Access LMU: | 31. Mrz. 2017, 09:09 |
Letzte Änderungen: | 04. Nov. 2020, 13:14 |