Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Free text analysis

P M Pietrzyk1

  • 1Department of Medical Informatics, Georg August University, Göttingen, Germany.

International Journal of Bio-Medical Computing
|April 1, 1995
PubMed
Summary

This study reviews automated methods for analyzing medical free text in hospital information systems (HIS). The Unified Medical Language System (UMLS) and SNOMED III are key to retrieving information from patient records.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

National rates and regional differences in sensitization to allergens of the standard series. Population-adjusted frequencies of sensitization (PAFS) in 40,000 patients from a multicenter study (IVDK).

Contact dermatitis·1997
Same author

A Medical Text Analysis System for German--syntax analysis.

Methods of information in medicine·1991
See all related articles

Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Health Information Systems

Background:

  • Medical free text analysis is crucial for extracting valuable data from patient records within hospital information systems (HIS).
  • Current research focuses on automated approaches for literature, case, and fact retrieval from unstructured clinical data.
  • The Unified Medical Language System (UMLS) project has significantly advanced the field of medical natural language processing.

Purpose of the Study:

  • To review automated approaches for medical free text analysis in HIS.
  • To highlight the role of the Unified Medical Language System (UMLS) in current research.
  • To discuss the potential standardization of knowledge sources like UMLS and SNOMED III for HIS data utilization.

Main Methods:

  • Literature review of automated approaches for medical free text analysis.
  • Analysis of the impact of the Unified Medical Language System (UMLS) on research.
  • Discussion of knowledge representation formalisms like conceptual graphs.

Main Results:

  • Automated methods are being developed for literature, case, and fact retrieval from HIS patient records.
  • The UMLS project has greatly stimulated research in medical free text analysis.
  • UMLS knowledge sources and SNOMED III show promise for standardizing HIS data utilization.

Conclusions:

  • Standardization of medical terminologies and knowledge sources is essential for effective HIS data utilization.
  • UMLS knowledge sources, SNOMED III, and conceptual graphs are potential standards for managing free text in HIS.
  • Further development and translation of these resources are needed to maximize their global impact.

Related Experiment Videos