Big data represents a ground-breaking opportunity for healthcare industry to improve service quality. Clinical Decision Support will help the clinician answer clinical questions quickly when information is needed, especially if the learning method is built into the Electronic Health Record (EHR). This paper delineates how unstructured data can be mined from different point of care devices to improve the prognosis and diagnosis of Chronic Kidney Disease (CKD).
This proposed mining system will scrape and analyze data from ubiquitous point of care devices and make predictions. This system will help predict potential events which may be symptoms of Kidney disease or a drug interaction that could lead to kidney problems. CKD is among the top ten leading causes of death in Canada according to Statistics Canada. This research will help clinicians in the areas of chronic disease management, prenatal patients monitoring, preventative medicine, and aggregation of medical information.