Review On Machine Learning Approach for Detecting Disease-Treatment Relations in Short Texts

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. Empirical domain of automatic learning is used in tasks such as medical decision support, protein-protein interaction, medical imaging, and extraction of medical knowledge. ML is envisioned as a tool by which computer-based systems can be integrated in the healthcare field in order to get a better and more efficient medical care ML-based methodology for building an application that is capable of identifying and disseminating healthcare in-formation. Due to advancements in medical domain automatic learning has gained popularity in the fields of medical decision support, complete health management and extraction of medical knowledge. The main objective of this work is to show what Natural Language Processing (NLP) and Machine Learning (ML) techniques used for representation of information and what classification algorithms are suitable for identifying and classifying relevant medical information in short texts. This paper describes how ML and NLP can be used for extracting knowledge from published medical papers. It acknowledges the fact those tools capable of identifying reliable information in the medical domain stand as building blocks for a healthcare system that is up-to-date with the latest discoveries. Our research focus on the diseases and treatment information and the relation that exists between these two entities.