Εργαστήριο Υπολογιστικής Βιο-Ιατρικής

AI-CARE

Κατηγορία: national

Τίτλος Έργου: Προηγμένα Συστήματα Διαχείρισης Γνώσης με Διαχείριση Διαδικτυακής Τεχνολογίας και

Φορέας Χρηματοδότησης: ΕΣΠΑ 2007-2013

Είδος έργου: ΣΥΝΕΡΓΑΣΙΑ 2011 - 11SYN-6-2009

Συντονιστής/Ανάδοχος: Ερευνητικό Πανεπιστημιακό Ινστιτούτο (E.Π.Ι.) Τηλεπικοινωνιακών Συστημάτων (Ι.Τ.Σ.) - Πολυτεχνείο Κρήτης

Εταίροι: Ινστιτούτο Πληροφορικής (Ι.Π) - Ίδρυμα Τεχνολογίας και Έρευνας (ΙΤΕ), ΝευροΑνάδραση (ΝΑ)- Κέντρο Ψυχοφυσιολογικής Εκπαίδευσης, ΙΑΤΡΙΚΟ ΚΡΗΤΗΣ – ΕΥΡΩΙΑΤΡΙΚΗ

Διάρκεια: 21/8/2013-30/10/2015

Ημ/νία Λήξης: 30/6/2015

Συνολικός προϋπολογισμός έργου: 430500

Προϋπολογισμός ΙΤΕ-ΙΠ: 101500

Web Site: http://www.intelligence.tuc.gr/ai-care/

Στόχος έργου: The aim of the project is to enhance the capabilities of state of the art medical information management systems and their support for information analysis and reasoning by exploiting the time dimension in the information possessed. This can be achieved by adding the concepts of time and change (evolution) in a rich semantics ontology representation, enabling context aware information analysis and reasoning based on evolution over time. State-of-the-art information representation and reasoning methods have limited expressive power for describing real world changing processes. A good application domain to demonstrate these issues is health where current search engines do not support finding associations with patients who have undergone the similar sequences of examinations or have similar diagnoses (or different diagnosis for the same disease) over a certain period of time. AI-CARE will enable these tasks by adding the ability to define time determined properties in an ontology model of medical knowledge, thus allowing time to affect the status of the described concepts. This might not only increase the quality of searches but also improve the quality of medical care for populations by statistical analysis on data (e.g., for finding similarities and regulations in patient care between different populations). However, finding patients with similar evolution (over time) of diseases, or patients who may have undergone the same or different treatment for the same diseases, is important information by itself that might lead to better treatment for individual patients.

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