Search results for "Multi dimensional"
showing 3 items of 13 documents
Multi-Dimensional motivic pattern extraction founded on adaptive redundancy filtering
2005
Abstract We present a computational model for discovering repeated patterns in symbolic representations of monodic music. Patterns are discovered through an incremental adaptive identification along a multi-dimensional parametric space. The difficulties of pattern discovery mainly come from combinatorial redundancies, that our model is able to control efficiently. A specificity relation is defined between pattern descriptions, unifying suffix and inclusion relations and enabling a filtering of redundant descriptions. Combinatorial proliferation caused by successive repetitions of patterns is managed using cyclic patterns. The modelling of these redundancy control mechanisms enables an autom…
One-Year Evolution of Symptoms and Health Status of the COPD Multi-Dimensional Phenotypes: Results from the Follow-Up of the STORICO Observational St…
2021
Raffaele Antonelli Incalzi,1 Francesco Blasi,2,3 Nicola Scichilone,4 Alessandro Zullo,5 Lucia Simoni,5 Giorgio Walter Canonica6 On Behalf of STORICO study group1Internal Medicine and Geriatrics Department Biomedical Campus University of Rome, Rome, Italy; 2Internal Medicine Department, Respiratory Unit and Cystic Fibrosis Adult Center, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Milan, Italy; 3Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy; 4DIBIMIS, University of Palermo, Palermo, Italy; 5Medineos Observational Research, Modena, Italy; 6Personalized Medicine Asthma & Allergy Clinic, Humanitas University, Humanitas &…
Dissecting the clinical heterogeneity of adult-onset Still's disease, results from a multi-dimensional characterisation and stratification
2021
Abstract Objectives To stratify adult-onset Still’s disease (AOSD) patients in distinct clinical subsets to be differently managed, by using a multi-dimensional characterization. Methods AOSD patients were evaluated by using a hierarchical unsupervised cluster analysis comprising age, laboratory markers systemic score and outcomes. The squared Euclidean distances between each pair of patients were calculated and put into a distance matrix, which served as the input clustering algorithm. Derived clusters were descriptively analysed for any possible difference. Results Four AOSD patients clusters were identified. Disease onset in cluster 1 was characterized by fever (100%), skin rash (92%) an…