Description

The Machine Learning (ML) field has gained its momentum in almost any domain of research and just recently has becomea reliable tool in the medical domain. The empirical domain of automatic learning is used in tasks such as medical decision support,medical imaging, protein-protein interaction, extraction of medical knowledge, and for overall patient management care. ML isenvisioned as a tool by which computer-based systems can be integrated in the healthcare field in order to get a better, more efficientmedical care. This paper describes a ML-based methodology for building an application that is capable of identifying and disseminatinghealthcare information. It extracts sentences from published medical papers that mention diseases and treatments, and identifiessemantic relations that exist between diseases and treatments. Our evaluation results for these tasks show that the proposedmethodology obtains reliable outcomes that could be integrated in an application to be used in the medical care domain. The potentialvalue of this paper stands in the ML settings that we propose and in the fact that we outperform previous results on the same data set.