Ontology Based Information Extraction for Disease Intelligence

TL;DR

This paper introduces an ontology-based framework for medical and disease intelligence that integrates heterogeneous data sources, genetic information, and disease characteristics to improve disease understanding and facilitate research.

Contribution

It presents a novel ontology model that combines disease taxonomy, genetic data, and symptom-drug mappings for enhanced disease information extraction.

Findings

Ontology enables rapid identification of emerging diseases.

System integrates genetic and symptomatic data effectively.

Supports researchers in exploring disease treatment options.

Abstract

Disease Intelligence (DI) is based on the acquisition and aggregation of fragmented knowledge of diseases at multiple sources all over the world to provide valuable information to doctors, researchers and information seeking community. Some diseases have their own characteristics changed rapidly at different places of the world and are reported on documents as unrelated and heterogeneous information which may be going unnoticed and may not be quickly available. This research presents an Ontology based theoretical framework in the context of medical intelligence and country/region. Ontology is designed for storing information about rapidly spreading and changing diseases with incorporating existing disease taxonomies to genetic information of both humans and infectious organisms. It further maps disease symptoms to diseases and drug effects to disease symptoms. The machine understandable disease ontology represented as a website thus allows the drug effects to be evaluated on disease symptoms and exposes genetic involvements in the human diseases. Infectious agents which have no known place in an existing classification but have data on genetics would still be identified as organisms through the intelligence of this system. It will further facilitate researchers on the subject to try out different solutions for curing diseases.