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Updated: Jul 31, 2025

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

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Published on: November 22, 2019

Biomedical heterogeneous data categorization and schema mapping toward data integration.

Priya Deshpande¹, Alexander Rasin², Roselyne Tchoua²

¹Marquette University, Milwaukee, WI, United States.

Frontiers in Big Data

|May 4, 2023

Summary

This summary is machine-generated.

Integrating diverse clinical data is crucial for healthcare advancement. This study presents a semantic categorization method to merge 88% of disparate clinical data, overcoming interoperability challenges.

Keywords:

data categorization data integration datasets heterogeneous data schema mapping semantic similarity unstructured data

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Area of Science:

Clinical data science
Health informatics
Biomedical data integration

Background:

Clinical data is often unstructured, heterogeneous, and spans various formats and standards.
Patient privacy constraints and diverse data structures within organizations complicate data integration.
Manual data integration by domain experts is time-consuming and costly.

Purpose of the Study:

To develop a method for categorizing and merging clinical data by considering underlying semantics.
To overcome challenges in clinical data interoperability and integration.
To reduce reliance on manual expert input for data integration.

Main Methods:

Mapping clinical text into common semantic categories.
Computing similarity measures within these categories.
Utilizing reference information for case-based data integration.

Main Results:

Successfully merged 88% of clinical data from five distinct sources.
Demonstrated the effectiveness of semantic categorization for data integration.
Validated the approach in a multi-source clinical data environment.

Conclusions:

The proposed method effectively integrates heterogeneous clinical data by leveraging semantic understanding.
This approach offers a scalable and cost-effective solution for clinical data interoperability.
Semantic-based data integration holds significant potential for advancing healthcare research and practice.