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

Automatic SNOMED classification--a corpus-based method

L M de Bruijn1, A Hasman, J W Arends

  • 1Department of Medical Informatics, Maastricht University, The Netherlands. berry@mi.unimaas.nl

Computer Methods and Programs in Biomedicine
|September 18, 1997
PubMed
Summary
This summary is machine-generated.

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

IMIA Accreditation of Biomedical and Health Informatics Education: Current State and Future Directions.

Yearbook of medical informatics·2017
Same author

Quality of health care: informatics foundations.

Yearbook of medical informatics·2016
Same author

Image and Signal Processing.

Yearbook of medical informatics·2016
Same author

Education and Research at the Department of Medical Informatics Maastricht.

Yearbook of medical informatics·2016
Same author

Computer-Aided Diagnosis of Abdominal Pain. (Reflections on F.T. De Dombal et al.'s paper: Computer-Aided Diagnosis of Acute Abdominal Pain).

Yearbook of medical informatics·2016
Same author

Computer supported patient management.

Technology and health care : official journal of the European Society for Engineering and Medicine·2014
Same journal

Accounting for approximation errors using surrogate-based parameter estimation of cardiac mechanics digital twins.

Computer methods and programs in biomedicine·2026
Same journal

Facial iPPG heatmap patterns based on period-aware autoencoder show association with carotid atherosclerosis towards non-contact hemodynamic assessment.

Computer methods and programs in biomedicine·2026
Same journal

Explainable machine learning models predict liver fibrosis risk and outcome in the general population: Development and multi-cohort external validation.

Computer methods and programs in biomedicine·2026
Same journal

Evaluation of surrogate endpoints for survival outcomes using the surrogate package in R.

Computer methods and programs in biomedicine·2026
Same journal

Relative spectral and frication-based descriptors as numerical indicators of place of articulation shifts in fricatives produced by Polish children.

Computer methods and programs in biomedicine·2026
Same journal

Leaflet resection improves valve expansion and hemodynamic performance in redo TAVI with balloon- and self-expanding transcatheter heart valve configurations.

Computer methods and programs in biomedicine·2026
See all related articles

This study introduces an automatic clinical narrative classification method using text comparison. The nearest neighbor approach successfully classified cases in 80-84% of trials, offering a versatile tool for diagnostic reports.

Area of Science:

  • Medical Informatics
  • Computational Linguistics
  • Digital Pathology

Background:

  • Clinical narrative classification is crucial for efficient healthcare data management.
  • Manual classification of diagnostic reports is time-consuming and prone to errors.
  • Automated methods are needed to improve the speed and accuracy of report categorization.

Purpose of the Study:

  • To present a novel method for automatic classification of clinical narratives.
  • To evaluate the effectiveness of a text comparison and nearest neighbor approach for diagnosis reports.
  • To provide a versatile computational tool for classifying medical texts.

Main Methods:

  • Developed an automatic classification method based on text comparison.
  • Utilized a 'nearest neighbor' algorithm to classify cases by searching for highly similar archive texts.

Related Experiment Videos

  • Conducted large-scale simulation experiments using diverse histology reports.

    • Main Results:

    • Achieved relevant classification in the top five alternatives for 80-84% of trials.
    • Encountered retrieval failures in 5% of cases due to missing relevant archive reports.
    • Demonstrated the method's capability in classifying a wide range of histology reports.

    Conclusions:

    • The proposed text comparison method is a versatile approach for automatic clinical narrative classification.
    • The nearest neighbor technique shows high accuracy for classifying diagnosis reports.
    • This automated system has the potential to significantly enhance medical record management and retrieval.