Diabetes care decision support system

Because of changing in lifestyle and diet, and lacking of moderate exercise, diabetes prevalence increases rapidly. Diabetes is an incurable chronic disease, but through proper care, patients can control the disease and prevent complications. According to patient's personal need, a proper diabetes care plan requires different fields of professionals together to make up the plan. So if it is done manually, it would restrict to the experience and knowledge of these professionals, and consume lots of expensive medical resources as well. The main purpose of this research is to use artificial intelligence techniques to generate personalized diabetes care plan. To do that, we first use case-based reasoning to search matched diabetes care plan in our case database according to personal condition and similarity function. The matched plan can be used directly or further modify it to better fit current problem. The modified case is saved in our case database for next time usage. The expansion of case database makes the system can propose care plan more in line with user requirement later. When there is no matched case, embedded in our system is a diabetes care ontology which can be consulted. The ontology generated a basic care plan to care professionals for adjustment. Again, this adjusted plan becomes the new case and the system deposits it in case database. The result of our research shows our system can provide personalized diabetes care plan efficiently. In the future, this system can be expanded to other diseases care and eventually becomes a total diseases care decision support system.