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Related Experiment Video

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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Adopting Graph Traversal Techniques for Context-Driven Value Sets Extraction from Biomedical Knowledge Sources.

Jyotishman Pathak, Guoqian Jiang, Sridhar O Dwarkanath

Proceedings. IEEE International Conference on Semantic Computing

|September 28, 2011

Summary

This summary is machine-generated.

This study introduces a novel context-driven approach for automatically extracting medical value sets from terminology services. The method links clinical context patterns to formal terminology models, improving data sharing and reuse across healthcare systems.

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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:

Medical Informatics
Health Information Systems
Computational Linguistics

Background:

Modeling, sharing, and reusing value sets across medical information systems is crucial.
Semi-automatic generation of value sets from terminology services remains challenging due to the lack of clinical context linkage.

Purpose of the Study:

To develop and evaluate a context-driven automatic value set extraction approach.
To address the limitations in defining concept domains and invoking value set extraction.

Main Methods:

Developed a formal terminology model-based approach for context-driven value set extraction.
Utilized two complementary methods: extensional (subject matter expert terms) and intensional (semantic concept definitions).
Employed graph traversal and ontology segmentation algorithms, implemented in a prototype using SNOMED CT and LexGrid.

Main Results:

Demonstrated the applicability of the approach on use cases from SNOMED CT within the LexGrid model.
Preliminary evaluation and investigation results were reported by subject matter experts at Mayo Clinic.

Conclusions:

The developed approach facilitates context-driven automatic value set extraction.
This method enhances the ability to model, share, and reuse value sets across diverse medical information systems.