A Collaborative CBR Recommender System to Support Patients, its Relatives and Caregivers in Chronic and Palliative Care

In the last decades, many medical assistance systems have been developed, and the interest in computer-aided problem-solving in medical and healthcare is constantly growing. Most of the software systems in this domain focus on decision support and recommendation of effective medication for patients. The combination of statistical analysis and case-based reasoning can facilitate a better medical diagnosis [1] [2] [3]. Within the case comparison mechanism of CBR, feature selection, similarity measurement, and adaptation methods play an important role to retrieve and revise cases. In this research, DePicT (Detect and Predict diseases using image classification and Text Information from patient health records) uses image interpretation and word associations for feature selection and recommendation of medical solutions [4]. All gathered patient records are stored in relational databases as structured or closed-format (e.g. parameters and statistics), or unstructured or open-format e.g. texts and images. For example, images of affected areas of a melanoma skin cancer can contribute and support early stage diagnosis. Also, further information on answering questions or writing a statement about the patient's health condition is added to the knowledge base. Domain Experts can validate and verify the collected information and also update the case-base to correct the data records of patients. In the other hand, more over than assisting for detecting and predict the disease, the Vocational Educational Training (VET) and Technology Enhanced Learning (TEL) [5] is a research field which is investigated continuously. DePicT CLASS (Detect and Predict diseases using image classification and Text information in Case-based Learning Assistant System) is a CBR system by enrichment of cases with learning materials (e.g. reference images and textbook) [6]. It is utilized smart (knowledge-based) and accessible systems to provide vocational educational learning opportunities and achieving higher education. CBR is applied in various problem-solving domains, and it is appropriate in medicine to integrate the system and for explicit experience, cognitive adequateness, the duality of objective/subjective knowledge, and to extract subjective knowledge [7]. Design and development of the DePicT and DePicT CLASS are the main contributions of this investigation. It is a case-based system which uses DePicT Profile Matrix of the association strength between title phrase and identified keywords of cases. Making experiments to validate the research and this recommender system lead us to do it in the