DEEP LEARNING ALGORITHM FOR CONNECTING SCIENTIFIC RECORDS AND SOCIAL PLATFORM

Abstract

In the healthcare industry, professionals develop big amounts of disorganized data. The complexity of this data and the loss of computational capability lead to delays in the investigation. Nevertheless, with the advent of Deep Learning algorithms and connection to computing power such as Graphic Processor Units (GPUs) and Tensor Processing Units (TPUs), text and image processing has become usable. Deep Learning (DL) data bring about a big outcome in Natural Language Processing (NLP) and computer perception. The main purpose of this study is to build an undivided approach that can relate social platforms, literature, and scientific records to develop an approach to medicinal education for the public and experts.
 This study focuses on NLP in the healthcare industry and compiles data by Electronic Medical Records (EMR), medical literature, and social platforms. The framework proposed in this study is one for connecting social platforms, medical literature, and Electronic Medical Records scientific records using Deep Learning algorithms. Linking data sources requires defining the relationships between them, and finding concepts in medical texts. The National Library of Medicine (NLM) introduced the Unified Medical Language System (UMLS) and uses this system as the basis for the proposed system. The dynamic nature of a social platform can be recognized and supervised methodologies can be applied under supervision to develop conception. Named entity Recognition (NER) enables the active eradication of data or individuals by the pharmaceutical literature.