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Updated: May 27, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

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

Published on: September 20, 2018

Recognizing Temporal Information in Korean Clinical Narratives through Text Normalization.

Youngho Kim1, Jinwook Choi

  • 1Interdesciplinary Program of Bioengineering, College of Engineering, Seoul National University, Seoul, Korea.

Healthcare Informatics Research
|November 16, 2011
PubMed
Summary
This summary is machine-generated.

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This study presents an effective system for extracting temporal information from noisy Korean clinical narratives. The developed method achieves high precision and recall, improving data accessibility in healthcare.

Area of Science:

  • Natural Language Processing
  • Clinical Informatics
  • Korean Language Processing

Background:

  • Clinical narratives contain time-sensitive information crucial for patient care.
  • Extracting temporal data from Korean clinical texts is challenging due to language complexity and noise.

Purpose of the Study:

  • To develop and evaluate a system for extracting temporal information from Korean clinical narrative texts.
  • To address the challenges posed by mixed-language, syntactically sparse, and noisy Korean clinical data.

Main Methods:

  • A two-stage system involving exhaustive text analysis and temporal expression recognition.
  • Utilized a corpus-based approach to decompose complex tokens into minimal semantic units.
  • Employed a finite state machine to identify time-related phrases within the analyzed units.
Keywords:
Automated Pattern RecognitionInformation ProcessingMedical InformaticsMedical RecordMultilingualism

Related Experiment Videos

Last Updated: May 27, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

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

Published on: September 20, 2018

Main Results:

  • The system successfully extracted temporal expressions from Korean clinical narratives.
  • Evaluated on 100 discharge summaries, the system achieved a phrase-level precision of 0.895 and recall of 0.919.
  • Demonstrated effectiveness in handling the complexities of Korean clinical text.

Conclusions:

  • The developed method is effective for acquiring temporal information from challenging Korean clinical documents.
  • The system offers a valuable tool for improving the accessibility and usability of time-sensitive clinical data.
  • Highlights the potential of NLP techniques in processing specialized, noisy language data.