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Related Experiment Video

Updated: May 11, 2026

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

Published on: May 19, 2019

A corpus-based approach for automated LOINC mapping.

Mustafa Fidahussein¹, Daniel J Vreeman

¹Regenstrief Institute, Inc, Indianapolis, Indiana, USA.

Journal of the American Medical Informatics Association : JAMIA

|May 17, 2013

Summary

This summary is machine-generated.

A rich dataset of local terms mapped to Logical Observation Identifiers Names and Codes (LOINC) can automate the mapping of terms for other institutions. This data-driven approach accelerates the adoption of vocabulary standards for better health information exchange.

Keywords:

LOINC automated mapping health information exchange information retrieval local laboratory tests supervised machine learning

Related Experiment Videos

Last Updated: May 11, 2026

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

Published on: May 19, 2019

Area of Science:

Medical Informatics
Natural Language Processing
Health Data Standards

Background:

Mapping local laboratory terms to standardized terminologies like LOINC is crucial for interoperability.
Existing methods for mapping local terms can be time-consuming and resource-intensive.
Leveraging existing knowledge within mapped corpora offers a potential solution for efficient mapping.

Purpose of the Study:

To assess if a corpus of local terms already mapped to LOINC can facilitate the mapping of local terms from different institutions.
To develop and evaluate automated methods for mapping local terms to LOINC.

Main Methods:

Developed two automated mapping models: a supervised machine learning model (Apache OpenNLP Maxent) and an information retrieval model (Apache Lucene).
Validated models using 20 iterations of 80/20 random subsampling for training and testing.
Evaluated model performance on laboratory terms from three distinct institutions.

Main Results:

The Maxent model ranked the correct LOINC code first for 70.5-71.4% of terms during validation and 73.5-84.6% (mean 78.9%) across test institutions.
The Lucene model achieved slightly lower performance, ranking the correct LOINC code first for 63.7-65.0% during validation and 66.5-76.6% (mean 71.9%) across test institutions.
Maxent consistently ranked the correct LOINC code first for over 57% of terms with a cut-off score of 0.46.

Conclusions:

A comprehensive corpus of LOINC-mapped local terms contains valuable collective knowledge for mapping terms across institutions.
Automated, data-driven mapping using freely available tools can significantly improve the efficiency and accuracy of term mapping.
Accelerated adoption of vocabulary standards through such methods promotes widespread health information exchange.