arXiv:1310.7505v1[stat.AP]28Oct2013 Quantifying age- and gender-related diabetes comorbidity risks using nation-wide big claims data Peter Klimek1 , Alexandra Kautzky-Willer2 , Anna Chmiel1 , Irmgard Schiller-Fr¨uhwirth3 , and Stefan Thurner1,4,5∗ 1 Section...
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arXiv:1310.7505v1[stat.AP]28Oct2013 Quantifying age- and gender-related diabetes comorbidity risks using nation-wide big claims data Peter Klimek1 , Alexandra Kautzky-Willer2 , Anna Chmiel1 , Irmgard Schiller-Fr¨uhwirth3 , and Stefan Thurner1,4,5∗ 1 Section for Science of Complex Systems, Medical University of Vienna, Spitalgasse 23, A-1090, Austria. 2 Gender Medicine Unit, Endocrinology & Metabolism, Dept. of Internal Medicine III, Medical University of Vienna, Spitalgasse 23, A-1090, Austria. 3 Main Association of Austrian Social Security Institutions, Kundmanngasse 21, A-1031, Austria. 4 Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA. 5 IIASA, Schlossplatz 1, A-2361 Laxenburg, Austria. ∗ (Dated: October 29, 2013) Currently emerging big data techniques are reshaping medical science into a data science. Medical claims data allow assessing an entire nations health state in a quantitative way, in particular with regard to the occurrences and consequences of chronic and
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