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The Efficiency of Different Distance Metrics for Keyword-Based Search in Medical Documents: A Short Case Study
Ágnes Vathy-Fogarassy1, Szabolcs Szekér1, Balázs Szolár1
1Department of Computer Science and Systems Technology, University of Pannonia, Hungary.
Studies in Health Technology and Informatics
|June 25, 2020
Summary
The Jaro-Winkler dissimilarity measure effectively processes medical texts, overcoming challenges like typos and abbreviations in keyword-based searches for improved content exploration.
Area of Science:
- Medical Informatics
- Natural Language Processing
Background:
- Medical texts present processing challenges due to errors, synonyms, and abbreviations.
- Accurate information retrieval from clinical notes is crucial.
Purpose of the Study:
- To evaluate common string similarity measures for keyword-based medical text search.
- To identify the most effective measure for analyzing medical document content.
Main Methods:
- Comparative analysis of string similarity algorithms.
- Application of measures to a large dataset of echocardiography reports.
Main Results:
- The Jaro-Winkler dissimilarity measure demonstrated superior performance.
- This measure effectively handles variations in medical text data.
- Over 20,000 echocardiography reports were analyzed.
Conclusions:
- Jaro-Winkler is highly capable for exploring medical text content.
- This finding aids in developing more robust medical information retrieval systems.
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