A CLINICAL DECISION SUPPORT PLATFORM BASED ON ONTOLOGIES AND CASE-BASED REASONING FOR ANTIBIOTICS PRESCRIPTION IMPLEMENTED BY A MULTI-AGENT SYSTEM

4^th International Conference on Medical Informatics & Telehealth

October 6-7, 2016 | London, UK

Ying Shen, Joel Colloc and Lei Kai

Peking University, China
University du Havre, France

Posters & Accepted Abstracts: J Health Med Informat

Abstract :

This study describes the construction and optimizes the sensitivity specificity of a decision support (DS) platform for identifying a potential infectious disease, according to a patient�s self-description of their disease state. A pilot domain ontology was constructed that pertain to clinical stages and their corresponding information components. The DS platform cooperates with ontology to use an estimate of the likelihood of achieving maximum benefit in each disease case to form empirical therapy recommendations and data on the sensitivity of the disease organism to antibiotics. If the disease severity is not too high, the DS platform screens for an appropriate therapy and proposes an antibiotic therapy specifically adapted to the patient, taking into account the indications, contraindications, side-effects, drug-drug interactions between proposed therapy and already prescribed medication and the route of administration of the therapy. Aiming to avoid drug-use risks as much as possible and screening for some antibiotic application protocols that are not in accordance with current medical theory, the DS platform uses case-based reasoning (CBR) to search for similar medical cases within the database and presents the references to the patient as justifiable evidence. The proposed DS platform supports NLP queries. Patients can obtain therapy suggestions by inquiring about a current clinical case. By combining a DS platform based on the therapeutic knowledge base, a diagnostic model of infectious disease, and a CBR approach via subtractive design, and also avoiding drug-use risks as much as possible, categorization of possible infectious disease diagnoses are suggested.

Biography :

Email: shenying@pkusz.edu.cn