Computational Learning in Biology and Medicine
No single data type can capture the complexity of all the factors relevant to identifying, understanding and classifying a disease. Computational learning, including traditional statistical analysis and innovative methods related to computer vision and artificial intelligence, is an integrative methodology that combines data from multiple technologies and, in biomedical application, can be used for modelling, identifying and classifying disorders and diseases related to physiological systems such as cardiovascular, motor and metabolic ones.
Laboratory: Cardiovascular Bioengineering Lab, Movement Analysis Lab, BioEngineering Lab
Contact Person: Prof.ssa Laura Burattini, Prof. Sandro Fioretti, Dott.ssa Micaela Morettini, Dott.ssa Federica Verdini and Dott. Francesco Di Nardo
  • Prof. Cees A. Swenne, Cardiology Department, Leiden University Medical Center (LUMC)
  • Dott. Giovanni Pacini e Dott. Andrea Tura, CNR, Istituto di Neuroscienze, Unità Metabolica
  • RESEARCH AGREEMENT 2017: Metodi e modelli matematici e statistici in ambito metabolico, research agreement con l'Istituto di Neuroscienze del Consiglio Nazionale delle Ricerche, Pisa
  • ERASMUS PROJECT 2017: Application of neural network techniques in serial electrocardiogram diagnosis: a feasibility study, research project con Leiden University Medical Center
  • PRA2018: Analisi dell'ECG tramite metodologie deep-learning