Souhir Ben Souissi1, Mourad Abed2, Lahcen El Hiki3, Philippe Fortemps4, Marc Pirlot5. 1. University of Haute-Alsace, ENSISA, 12 Rue des Frères Lumière, 68093 Mulhouse, France. Electronic address: Souhir.Ben-souissi@uha.fr. 2. University Polytechnic of Hauts de France, LAMIH, Aulnoy lez Valenciennes, 59313 Valenciennes Cedex 9, France. Electronic address: mourad.abed@univ-valenciennes.fr. 3. University of Mons, Research Institute for the Science and Management of Risks, 20, place du Parc, B7000 Mons, Belgium. Electronic address: Lahcen.Elhiki@umons.ac.be. 4. University of Mons, Faculty of Engineering, 9, rue de Houdain, B7000 Mons, Belgium. Electronic address: Philippe.Fortemps@umons.ac.be. 5. University of Mons, Faculty of Engineering, 9, rue de Houdain, B7000 Mons, Belgium. Electronic address: Marc.Pirlot@umons.ac.be.
Abstract
OBJECTIVE: Motivated by the well documented worldwide spread of adverse drug events, as well as the increased danger of antibiotic resistance (caused mainly by inappropriate prescribing and overuse), we propose a novel recommendation system for antibiotic prescription (PARS). METHOD: Our approach is based on the combination of semantic technologies with MCDA (Multiple Criteria Decision Aiding) that allowed us to build a two level decision support model. Given a specific domain, the approach assesses the adequacy of an alternative/action (prescription of antibiotic) for a specific subject (patient) with an issue (bacterial infection) in a given context (medical). The goal of the first level of the decision support model is to select the set of alternatives which have the potential to be suitable. Then the second level sorts the alternatives into categories according to their adequacy using an MCDA sorting method (MR-Sort with Veto) and a structured set of description logic queries. RESULTS: We applied this approach in the domain of antibiotic prescriptions, working closely with the EpiCura Hospital Center (BE). Its performance was compared to the EpiCura recommendation guidelines which are currently in use. The results showed that the proposed system is more consistent in its recommendations when compared with the static EpiCura guidelines. Moreover, with PARS the antibiotic prescribing workflow becomes more flexible. PARS allows the user (physician) to update incrementally and dynamically a patient's profile with more information, or to input knowledge modifications that accommodate the decision context (like the introduction of new side effects and antibiotics, the development of germs that are resistant, etc). At the end of our evaluation, we detail a number of limitations of the current version of PARS and discuss future perspectives.
OBJECTIVE: Motivated by the well documented worldwide spread of adverse drug events, as well as the increased danger of antibiotic resistance (caused mainly by inappropriate prescribing and overuse), we propose a novel recommendation system for antibiotic prescription (PARS). METHOD: Our approach is based on the combination of semantic technologies with MCDA (Multiple Criteria Decision Aiding) that allowed us to build a two level decision support model. Given a specific domain, the approach assesses the adequacy of an alternative/action (prescription of antibiotic) for a specific subject (patient) with an issue (bacterial infection) in a given context (medical). The goal of the first level of the decision support model is to select the set of alternatives which have the potential to be suitable. Then the second level sorts the alternatives into categories according to their adequacy using an MCDA sorting method (MR-Sort with Veto) and a structured set of description logic queries. RESULTS: We applied this approach in the domain of antibiotic prescriptions, working closely with the EpiCura Hospital Center (BE). Its performance was compared to the EpiCura recommendation guidelines which are currently in use. The results showed that the proposed system is more consistent in its recommendations when compared with the static EpiCura guidelines. Moreover, with PARS the antibiotic prescribing workflow becomes more flexible. PARS allows the user (physician) to update incrementally and dynamically a patient's profile with more information, or to input knowledge modifications that accommodate the decision context (like the introduction of new side effects and antibiotics, the development of germs that are resistant, etc). At the end of our evaluation, we detail a number of limitations of the current version of PARS and discuss future perspectives.