Statistical methods in cardiovascular physiology

The past two decades have seen impressive success in medical technology, generating novel experimental data at an unexpected rate.
However, current computational methods cannot sufficiently manage the data analysis for interpretation, so that clinical application is
hindered and the benefit for the patient is still small. Even though numerous physiological models have been developed to describe complex
dynamical mechanisms, their clinical application is limited, because parameterization is crucial and most problems are ill-posed and do not have unique solutions. However, this information deficit is immanent to physiological data, because the measurement process always contains
contamination like artifacts or noise and is limited by a finite measurement precision. The proposed cardiovascular system identification procedure allows us to deduce patient-specific
information, that can be used to diagnose a variety of cardiovascular diseases in an early state. In contrast to traditional inversion
approaches the novel method produces a distribution of physiologically interpretable models (patient-specific parameters and model states) that allow the identification of disease specific patterns that correspond to clinical diagnoses, enabling a probabilistic assessment
of human health condition on the basis of a broad patient population. In the ongoing work we use this technique to identify arterial stenosis and aneurisms from anomalous patterns in signal and parameter space.

E gradita la vostra partecipazione.

Barbara Martinelli
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Segreteria MOX