Conformance Analysis for Comorbid Patients in Answer Set Programming Abstract   The treatment of comorbid patients is a hot problem in Medical Informatics, since the plain application of multiple ComputerInterpretable Guidelines (CIGs) can lead to interactions that are potentially dangerous for the patients. The specialized literature has mostly focused on the xe2x80x9capriorixe2x80x9d or xe2x80x9cexecutiontimexe2x80x9d analysis of the interactions between multiple ComputerInterpretable Guidelines (CIGs), andor CIG xe2x80x9cmergexe2x80x9d. In this paper, we face a complementary problem, namely, the aposteriori analysis of the treatment of comorbid patients. Given the CIGs, the history of the status of the patient, and the log of the clinical actions executed on her, we try to explain the actions executed on the patient (i.e., the log) in terms of the actions recommended by the CIGs, of their potential interactions, and of the possible ways of managing each such interaction, pointing out (i) deviations from CIG recommendations not explained in terms of interaction management (if any) and (ii) unmanaged interactions (if any). Our approach is based on Answer Set Programming, and, to face realistic problems, devotes specific attention to the temporal dimension.

Personalised drug prescription for dental clinics using word embedding The number of drugs in drug databases is constantly expanding with novel drugs appearing on the market each year. A dentist cannot be expected to recall all the drugs available, let alone potential drugdrug interactions (DDI). This can be problematic when dispensing drugs to patients especially those with multiple medical conditions who often take a multiple medications. Any new  :59,must be checked against the patientxe2x80x99s medical history, in order to avoid drug allergies and reduce the risk of adverse reactions. Current drug databases allowing the dentist to check for DDI are limited by the lack of integration of the patientxe2x80x99s medical profile with the drug to be prescribed. Hence, this paper introduces a software which predicts the possible DDI of a new medication  patientxe2x80x99s medical :124,profile, based on previous findings that associate similarity ratio with DDI. This system is based conceptually on a threetier framework consisting of a knowledge layer, prediction layer and presentation layer. The novel approach of this system in applying feature vectors for drug prescription will be demonstrated during the conference (http:r.glory.sgmain.php). By engaging with the interactive demonstration, participants will gain firsthand experience in the process from  to :192,implementation. Future work includes the extension of use from dental to medical institutions, and it is currently being enhanced to serve as a training tool for medical students.