A Recommendation System for Electronic Health Records in the Context of the HOPE Project

This paper proposes a new recommendation system in the context of the HOPE project with the aim of providing medical bibliographic references in a simple, up-to-date and immediate way. In addition, these references are catalogued according to the patient information and symptoms, offering a ranking mechanism which sorts them from most interesting to least relevant ones according to the feedback provided by health professionals. The proposed system has been extensively trained and validated with a set of widely used machine learning models, particularly Random Forest (RF), Multinomial Logistic Regression (MLR) and Support Vector Machines (SVMs). The results obtained over real medical data from HOPE project are quite promising, exhibiting a high precision. In particular, RF is the algorithm which the best behavior with a 89.9% of precision. It is closely followed by the SVM, which reaches great results with a 89.4% of precision, performing quite accurately with false negative cases.