Content and context based similarity for disease diagnosis

When there is a new patient, medical experts usually compare the results with those of previously seen cases for diagnosis and treatment. Besides, the number of digital medical images acquired, stored and managed at the healthcare centres is exponentially increasing each day with the advances in the medical imaging technology. Therefore, automated search and retrieval of similar casesfrom large medical databases may especially improve diagnosis and treatment of diseases whose causes and effects have not yet been fully known. Database management systems used at clinical sites store textual information like name, age, and gender along with medical images. Accordingly, combined usage of image features (content) with demographic and clinical information (context) for similar case search may improve accuracy andreliability of diagnosis, and thus result in increased patient care and lower healthcare costs. In this study we explored the effect of content features extracted from magnetic resonance images and context features such as patient demographics and clinical data on similarity-based disease diagnosis. Our validation on data composed of Alzheimer, Parkinson, Dementia and healty cases showed that combined usage of content and context provides more accurate diagnosis with less features.