We present a pilot data mining analysis on the subset of the Public Health Database (PHD) of Lombardia Region concerning hospital discharge data relative to Acute Myocardial Infarctions without ST segment elevation (NON-STEMI). The analysis is carried out using nonlinear semi-parametric and parametric mixed effectsmodels, in order to detect different patterns of growth in the number of NON-STEMI diagnoses within the 30 largest clinical structures of Lombardia Region, along the time period 2000–2007. The analysis is a seminal example of statistical support to decision makers in clinical context, aimed at monitoring the diffusion of new procedures and the effects of health policy interventions.

Mining Administrative Health Databases for Epidemiological Purposes: A Case Study on Acute Myocardial Infarctions Diagnoses

IEVA, FRANCESCA;PAGANONI, ANNA MARIA;SECCHI, PIERCESARE
2013-01-01

Abstract

We present a pilot data mining analysis on the subset of the Public Health Database (PHD) of Lombardia Region concerning hospital discharge data relative to Acute Myocardial Infarctions without ST segment elevation (NON-STEMI). The analysis is carried out using nonlinear semi-parametric and parametric mixed effectsmodels, in order to detect different patterns of growth in the number of NON-STEMI diagnoses within the 30 largest clinical structures of Lombardia Region, along the time period 2000–2007. The analysis is a seminal example of statistical support to decision makers in clinical context, aimed at monitoring the diffusion of new procedures and the effects of health policy interventions.
Advances in Theoretical and Applied Statistics
9783642355875
Biostatistics and bioinformatics; Data mining; Generalized linear mixed models; Health service research
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/643738
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