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UMLS knowledge for biomedical language processing

A T McCray¹, A R Aronson, A C Browne

¹Computer Science Branch, National Library of Medicine, Bethesda, MD 20894.

Bulletin of the Medical Library Association

|April 1, 1993

Summary

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This study developed the SPECIALIST natural language processing system to improve access to biomedical text. It uses the Unified Medical Language System (UMLS) for better information retrieval from medical databases.

Area of Science:

Biomedical Informatics
Natural Language Processing

Background:

Accessing free text in biomedical databases is challenging.
Developing effective information retrieval systems is crucial for medical research.

Purpose of the Study:

To describe the development of the SPECIALIST system for biomedical natural language processing.
To enhance access to and retrieval of information from biomedical databases.

Main Methods:

Developed SPECIALIST, an experimental natural language processing system.
Integrated a broad-coverage parser, large lexicon, and Unified Medical Language System (UMLS) Knowledge Sources.
Implemented a retrieval module for information retrieval experiments.

Main Results:

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The SPECIALIST system provides access to biomedical free text.

Utilized UMLS Metathesaurus and Semantic Network for concept relationships.

Investigated information requirements for mapping query language to document language.

Conclusions:

The SPECIALIST system facilitates biomedical information retrieval.
Mapping between query and document language is complex and requires inference.
Enhanced access to biomedical data is achievable through advanced NLP systems.