Use of machine learning for improvement of similarity searches of patients

Abstract

Due to various factors (human error, broken communication links) patient information within a healthcare enterprise varies for a given patient Medical personnel and technicians are often faced with the problem of searching similar patients within the hospital information system. The heterogeneity of attributes on which the search is performed increases the difficulty of retrieving conclusive results, thus simple search rules are unsatisfactory in this case. The work presented in this paper compares different machine learning algorithms for improving the similarity rating based on a set of human decisions.