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Updated: May 31, 2026

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

Using machine learning for concept extraction on clinical documents from multiple data sources.

Manabu Torii¹, Kavishwar Wagholikar, Hongfang Liu

¹Lab of Text Intelligence in Biomedicine, Georgetown University Medical Center, Washington, DC 20007, USA. torii@isis.georgetown.edu

Journal of the American Medical Informatics Association : JAMIA

|June 29, 2011

Summary

This summary is machine-generated.

Machine learning taggers for clinical concept extraction show reduced performance when moved between data sources. Training taggers on multiple data sources improves their robustness and accuracy in identifying clinical concepts.

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Area of Science:

Natural Language Processing
Machine Learning
Clinical Informatics

Background:

Concept extraction is vital for automated text processing in healthcare.
Evaluating machine learning tagger portability across diverse clinical data sources is crucial.

Purpose of the Study:

To investigate the performance and portability of machine learning taggers for clinical concept extraction.
To assess how well taggers trained on one data source perform on others.

Main Methods:

Utilized BioTagger-GM to train machine learning taggers.
Evaluated taggers on annotated clinical documents from the 2010 i2b2/VA Challenge workshop (four data sources).

Main Results:

Tagger performance degraded when evaluated on different data sources, with varying degrees of degradation.
A tagger trained on multiple data sources demonstrated robustness, achieving an F score of 0.890 on one source.
Increased annotated documents for training are likely to improve tagger performance.

Conclusions:

Machine learning tagger performance degrades when ported across clinical document sources.
Training taggers on multi-source datasets enhances their portability.
BioTagger-GM is adaptable for clinical concept extraction with strong performance.