The proposed eHealth Recommendation Service System (eHeaRSS) is to recommend health service information to patients whenever and wherever. Nowadays, depending on online networks popularity, there are a lot of research starting to focus on patient-context to apply health care fields like tele-health, tele-care, medical traveling, and so on. The proposed eHeaRSS is like a family doctor to care patients, especially to use generated patients-context. eHeaRSS is a recommendation system for immediate and appropriate medical services using crowd sourcing in cloud computing environment. To do this, it is necessary to classify the data into symptoms, diseases, departments and doctors related data types. To consider hierarchies or subsume relationships among them, eHeaRSS Ontology (eHeaRSS-Ont) is developed through integration of 4-static ontology (4-Ont) which is comprised of symptom-, disease-, department-and doctor-Ont to create tailored recommendations depending on patient-context. For the recommendation service, Case-based reasoning (CBR) is applied. The extracted cases using 4-Ont are integrated by eHeaRSS-Ont depending on the patient-context. For customization, it is necessary to reconfigure the case using eHeaRSS Constraints Value Compatibility Map (eHeaRSS-CVCM). To prove the significance and efficiency of the eHeaRSS, we experimented using ontology-based data processing and proved the superiority of eHeaRSS with the provision of better recommendation than other DB-based systems.
['Hyun Jung Lee', 'Hee Sun Kim']
eHealth Recommendation Service System Using Ontology and Case-Based Reasoning