The basic storage infrastructure used to gather data from the technological evolution also in the healthcare field was leading to the storing into public or private repository of even higher quantities of data related to patients and their pathological evolution. Big data techniques are spreading also in medical research. By these techniques is possible extract information from complex heterogeneous sources, realizing longitudinal studies focused to correlate the patient status with biometric parameters. In our work we develop a common data infrastructure involving 4 clinical dialysis centers between Lombardy and Switzerland. The common platform has been build to store large amount of clinical data related to 716 dialysis session of 70 patient. The platform is made up by a combination of a MySQL®database (Dialysis Database) and a MATLAB-based mining library (Dialysis MATlib). A statistical analysis of these data has been performed on the data gathered. These analyses led to the development of two clinical indexes, representing an example of transformation of big data into clinical information.

How to extract clinically useful information from large amount of dialysis related stored data

VITO, DOMENICO;CASAGRANDE, GIUSTINA;BIANCHI, CAMILLA;COSTANTINO, MARIA LAURA
2015

Abstract

The basic storage infrastructure used to gather data from the technological evolution also in the healthcare field was leading to the storing into public or private repository of even higher quantities of data related to patients and their pathological evolution. Big data techniques are spreading also in medical research. By these techniques is possible extract information from complex heterogeneous sources, realizing longitudinal studies focused to correlate the patient status with biometric parameters. In our work we develop a common data infrastructure involving 4 clinical dialysis centers between Lombardy and Switzerland. The common platform has been build to store large amount of clinical data related to 716 dialysis session of 70 patient. The platform is made up by a combination of a MySQL®database (Dialysis Database) and a MATLAB-based mining library (Dialysis MATlib). A statistical analysis of these data has been performed on the data gathered. These analyses led to the development of two clinical indexes, representing an example of transformation of big data into clinical information.
hemodialysis, personalized therapy, clinical data, statistical analysis
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/965084
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