PARS, a system combining semantic technologies with multiple criteria decision aiding for supporting antibiotic prescriptions.

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.

Reducing the Toxicity Risk in Antibiotic Prescriptions by Combining Ontologies with a Multiple Criteria Decision Model.

We consider the risk of adverse drug events caused by antibiotic prescriptions. Antibiotics are the second most common cause of drug related adverse events and one of the most common classes of drugs associated with medical malpractice claims. To cope with this serious issue, physicians rely on guidelines, especially in the context of hospital prescriptions. Unfortunately such guidelines do not offer sufficient support to solve the problem of adverse events. To cope with these issues our work proposes a clinical decision support system based on expert medical knowledge, which combines semantic technologies with multiple criteria decision models. Our model links and assesses the adequacy of each treatment through the toxicity risk of side effects, in order to provide and explain to physicians a sorted list of possible antibiotics. We illustrate our approach through carefully selected case studies in collaboration with the EpiCURA Hospital Center in Belgium.

A new decision support model for preanesthetic evaluation.

BACKGROUND AND OBJECTIVE: The principal challenges in the field of anesthesia and intensive care consist of reducing both anesthetic risks and mortality rate. The ASA score plays an important role in patients' preanesthetic evaluation. In this paper, we propose a methodology to derive simple rules which classify patients in a category of the ASA scale on the basis of their medical characteristics. METHODS: This diagnosis system is based on MR-Sort, a multiple criteria decision analysis model. The proposed method intends to support two steps in this process. The first is the assignment of an ASA score to the patient; the second concerns the decision to accept-or not-the patient for surgery. RESULTS: In order to learn the model parameters and assess its effectiveness, we use a database containing the parameters of 898 patients who underwent preanesthesia evaluation. The accuracy of the learned models for predicting the ASA score and the decision of accepting the patient for surgery is assessed and proves to be better than that of other machine learning methods. Furthermore, simple decision rules can be explicitly derived from the learned model. These are easily interpretable by doctors, and their consistency with medical knowledge can be checked. CONCLUSIONS: The proposed model for assessing the ASA score produces accurate predictions on the basis of the (limited) set of patient attributes in the database available for the tests. Moreover, the learned MR-Sort model allows for easy interpretation by providing human-readable classification rules.