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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
Published on: September 20, 2018
Medical facts to support inferencing in natural language processing
Thomas C Rindflesch1, Serguei V Pakhomov, Marcelo Fiszman
1National Library of Medicine, Bethesda, Maryland, USA.
AMIA ... Annual Symposium Proceedings. AMIA Symposium
|June 17, 2006
Summary
Medical facts enhance biomedical text processing. A repository of drug-disorder co-occurrences from clinical notes validates semantic interpretation of medical literature, improving accuracy in disease treatment research.
Area of Science:
- Biomedical Natural Language Processing
- Medical Informatics
- Computational Linguistics
Background:
- Semantic interpretation of biomedical text relies on external knowledge sources.
- A comprehensive fact repository is crucial for accurate inferencing in medical natural language processing.
Purpose of the Study:
- To enhance natural language processing (NLP) of biomedical text using medical facts.
- To construct and utilize a drug-disorder co-occurrence repository for validating semantic interpretations.
Main Methods:
- Statistical methods were employed to build a repository of drug-disorder co-occurrences from clinical notes.
- This repository was used to validate inferences from semantic interpretation of Medline citations.
- Evaluation was performed against a published reference standard for disease treatment.
Main Results:
- The developed repository effectively supports the validation of automatically drawn inferences.
- The approach demonstrated improved accuracy in semantic interpretation of biomedical literature.
- Results showed the utility of clinical notes for building factual resources.
Conclusions:
- Medical facts, particularly drug-disorder co-occurrences, are essential for enhancing biomedical NLP.
- A repository derived from clinical notes can significantly improve the semantic interpretation of medical literature.
- This methodology offers a robust way to validate and refine NLP systems in healthcare.
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