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Modeling data access legislation with Gorgias

Spanoudakis Nikolaos, Constantinou Elena, Koumi Adamos, Kakas, Antonis C

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/29961591-38DD-462F-A2EC-17967EFF9C82-
Αναγνωριστικόhttps://link.springer.com/chapter/10.1007%2F978-3-319-60045-1_34-
Αναγνωριστικόhttps://doi.org/10.1007/978-3-319-60045-1_34-
Γλώσσαen-
Μέγεθος11 pagesen
ΤίτλοςModeling data access legislation with Gorgiasen
ΔημιουργόςSpanoudakis Nikolaosen
ΔημιουργόςΣπανουδακης Νικολαοςel
ΔημιουργόςConstantinou Elenaen
ΔημιουργόςKoumi Adamosen
ΔημιουργόςKakas, Antonis Cen
ΕκδότηςSpringer Verlagen
ΠερίληψηThis paper uses argumentation as the basis for modeling and implementing the relevant legislation of an EU country relating to medical data access. Users can consult a web application for determining their allowed level of access to a patient’s medical record and are offered an explanation based on the relevant legislation. The system can also advise a user on what additional information is required for a higher access level. The system is currently in the process of an extensive evaluation through a pilot trial with a special focus group of medical professionals. The development methodology that we have used is generally applicable to any other similar cases of decision making based on legislative regulations. The main advantage of using argumentation is the ability to explain the solutions drawn and the high modularity of software facilitating the extension and adaptation of the system when new relevant legislation becomes available.en
ΤύποςΠλήρης Δημοσίευση σε Συνέδριοel
ΤύποςConference Full Paperen
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2018-05-31-
Ημερομηνία Δημοσίευσης2017-
Θεματική ΚατηγορίαArgumentationen
Θεματική ΚατηγορίαLegal systemsen
Θεματική ΚατηγορίαModular softwareen
Βιβλιογραφική ΑναφοράN. I. Spanoudakis, E. Constantinou, A. Koumi and A. C. Kakas, "Modeling data access legislation with Gorgias," in 30th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, 2017, pp. 317-327. doi: 10.1007/978-3-319-60045-1_34en

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