528 Computational Methods for Predictive Cardiovascular Disease
Nenad Filipovic, University of Kragujevac, Serbia
The current medicine treatment relies exclusively on diagnostic imaging data to define the present state of the patient, biomarkers and experience of the medical doctors to evaluate the efficacy of prior treatments for similar patients. Computational methods may give opportunity for a patient-specific model in order to improve the prediction for the disease progression. This minisymposium will include computational methods for cardiovascular disease prediction with emphasize on biomechanics, biomedical decision support system, data mining, personalized diagnostics, bio-signal processing, protein structure prediction, biomedical image processing, analysis and visualization, high performance computing and nanomedicine.
Authors will present with advanced research support tools for disease characterization, and the discovery of new knowledge; associations among heterogeneous data, that can improve the predictive power of the patient-model. Special part will be devoted to atherosclerosis disease, by standardizing and integrating heterogeneous health data, including those from key enabling technologies, and existing patient/artery specific multiscale and multilevel predictive models including cellular/molecular inflammatory markers.
Authors will present with advanced research support tools for disease characterization, and the discovery of new knowledge; associations among heterogeneous data, that can improve the predictive power of the patient-model. Special part will be devoted to atherosclerosis disease, by standardizing and integrating heterogeneous health data, including those from key enabling technologies, and existing patient/artery specific multiscale and multilevel predictive models including cellular/molecular inflammatory markers.
