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Semantic Integration of Heterogeneous Data Sources Using Ontology-Based Domain Knowledge Modeling for Early Detection

R Thirumahal¹, G Sudha Sadasivam¹, P Shruti¹

¹Department of Computer Science and Engineering, P.S.G College of Technology, Coimbatore, India.

SN Computer Science

|August 15, 2022

Summary

This study introduces an automatic ontology-based data integration model for healthcare. It effectively identifies patients at moderate to higher risk of severe COVID-19 illness from diverse data sources.

Keywords:

Attribute mapping Data heterogeneity Global and local schemas Healthcare domain Ontology based data retrieval

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Area of Science:

Biomedical Informatics
Data Science
Healthcare Technology

Background:

Explosion of biomedical data from diverse, decentralized sources presents integration challenges.
Heterogeneous data requires advanced integration methods for unified access and faster retrieval.
Existing solutions include ontology, machine learning, deep learning, and fuzzy logic approaches.

Purpose of the Study:

To develop an automatic ontology-based data integration model for the healthcare domain.
To address challenges in accessing and unifying semantically, structurally, and syntactically different biomedical data.
To facilitate the identification of patients at risk for severe COVID-19.

Main Methods:

A three-phase model for automatic ontology-based data integration.
Phase 1: Automatic data mapping and local ontology generation across heterogeneous sources (SQL, MongoDB, Excel).
Phase 2: Creation of a root global schema mapping by combining local ontologies.
Phase 3: Querying diverse databases to retrieve semantically analogous records, focusing on patient medical records, chest X-ray details, and COVID-19 symptom data.

Main Results:

Successfully integrated data from disparate sources (SQL, MongoDB, Excel).
Developed a functional ontology-based model for heterogeneous data integration in healthcare.
Identified patients with moderate/higher risk of developing serious illness from COVID-19 based on integrated data.

Conclusions:

The proposed automatic ontology-based data integration model is effective for the healthcare domain.
This approach enhances access to heterogeneous biomedical data, enabling faster and easier retrieval.
The model successfully facilitates the identification of at-risk patients for COVID-19, supporting clinical decision-making.