A Framework for Automatic Categorization of Social Data Into Medical Domains

Nowadays, everyone is surrounded by a large volume of data which is generated from multiple sources. Every day 2.5 quintillion bytes of data are created worldwide. It is a very complicated task to manage and extract useful information from social media platforms. As people are more interested in seeking online health guidance, the medical textual data created by patients, healthcare professionals, and medical staff are expanding fast. In order to address this challenge, an ontology-based approach is used to annotate the medical domain data in this article. We have considered Twitter as our social media platform to filter the medical data from the general category data. In this article, we have used the Open Biomedical Annotator (OBA), which is an ontology-based Web service that annotates raw text with biomedical ontology concepts based on their textual metadata. Domain-specific knowledge is gathered by using MedlinePlus Health Topics and National Drug Data File (NDDF) ontologies licensed under the U. S. National Library of Medicine (UMLS), which will filter out all medical related terms. Furthermore, these medical terms are stored in a medical term database, which will then be forwarded to the UMLS REST Application Programming Interface (API) that provides important entities, such as Concept Unique Identifier (CUIs), semantic relations, attributes, definitions, and much more under the subset of SNOMEDCT_US which is the most precise and comprehensive health terminology platform in the world.