Data Processing for Disease-Treatment Relations

The Machine Learning (ML) field has gained its momentum in almost any domain of research and just recently has become a 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 overall patient management care.

ML is envisioned as a tool by which computer-based systems can be integrated into the healthcare field in order to get better, more efficient medical care.

We describe an ML-based methodology for building an application that is capable of identifying and disseminating healthcare information.

It extracts sentences from published medical papers that mention diseases and treatments and identifies semantic relations that exist between diseases and treatments.

Our evaluation results for these tasks show that the proposed methodology obtains reliable outcomes that could be integrated into an application to be used in the medical care domain.

The potential value of this article stands in the ML settings that we propose and in the fact that we outperform previous results on the same data set.