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The paper deals with the problem of reconstructing a continuous one-dimensional function from discrete noisy samples. The measurements may also be indirect in the sense that the samples may be the output of a linear operator applied to the function (linear inverse problem, deconvolution). In some cases the linear operator could even contain unknown(More)
Therapy planning is a very complex task, being the patient's therapeutic response aaected by several sources of uncertainty. Furthermore , the modelling of a patient's evolution is frequently hampered by the incompleteness of the medical knowledge; it is hence often not possible to derive a mathematical model that is able to take into account the(More)
The minimal model of glucose kinetics, in conjunction with an insulin-modified intravenous glucose tolerance test, is widely used to estimate insulin sensitivity (S(I)). Parameter estimation usually resorts to nonlinear least squares (NLS), which provides a point estimate, and its precision is expressed as a standard deviation. Applied to type 2 diabetic(More)
BACKGROUND Mass spectrometry is an essential technique in proteomics both to identify the proteins of a biological sample and to compare proteomic profiles of different samples. In both cases, the main phase of the data analysis is the procedure to extract the significant features from a mass spectrum. Its final output is the so-called peak list which(More)
OBJECTIVE This paper describes the temporal data mining aspects of a research project that deals with the definition of methods and tools for the assessment of the clinical performance of hemodialysis (HD) services, on the basis of the time series automatically collected during hemodialysis sessions. METHODS Intelligent data analysis and temporal data(More)
This paper describes the application of a method for the intelligent analysis of clinical time series in the diabetes mellitus domain. Such a method is based on temporal abstractions and relies on the following steps: (i) 'pre-processing' of raw data through the application of suitable filtering techniques: (ii) 'extraction' from the pre-processed data of a(More)
We present a multi-modal reasoning (MMR) methodology that integrates case-based reasoning (CBR), rule-based reasoning (RBR) and model-based reasoning (MBR), meant to provide physicians with a reliable decision support tool in the context of type 1 diabetes mellitus management. In particular, we have implemented a decision support system that is able to(More)
Several studies have shown that patients suffering from Diabetes Mellitus can significantly delay the onset and slow down the progression of diabetes micro- and macro-angiopathic complications through intensive monitoring and treatment. In general, intensive treatments imply a careful blood glucose level (BGL) self-monitoring. The analysis of BGL(More)