Indian Journal of Science and Technology

Abstract

Objectives: To bridge the vocabulary gap between health seekers and providers to provide believable and accurate solutions to the health seekers for patients to solve issues over medical assistance records. Method: The proposed scheme consists of two mutually reinforced components, namely, local mining and global learning. The local mining technique is using some of the particular medical records for explaining the overall benefit and conclusion regarding particular health mapping by authentication terminology. The database of local Mining is getting update through the data of global learning, which collects the related medical data. This attempt is a particular pair of Q&A independently over the concept of medical extraction from pairing it through Q&A and maps it through the terminologies of authentication. Findings: In this research work a novel scheme is proposed which will be able to code the medical records with corpus-aware terminologies. As the first of its kind on automatically coding the community generated health data using concept entropy impurity approach to comparatively detect and normalize the medical concepts locally, which naturally construct a corpus-aware terminology vocabulary with the help of external knowledge. Also it builds a novel global learning model to collaboratively enhance the local coding results. This model seamlessly integrates various heterogeneous information cues. For making a communication or connection between the gaps, it is being described over here about the label of Question and Answer by joining and utilizing the approaches of global learning and local mining. Applications: The procedure can be used to build an efficient web portal for health seekers. The effectiveness of the system is being increased by experts question and answering.

Keywords: Corpus Aware Terminology, Global Learning, Health Care, Health Seekers, Local Mining, Semantic Approach