A Field Theoretical Approach to Medical Natural Language Processing

A parser for medical free text reports has been developed that is based on a chemistry/physics inspired ldquofield theoryrdquo for word-word sentence-level dependencies. The transition from the linguistic world to the world of interacting particles with potential energies is guided by a psycholingui...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2007-07, Vol.11 (4), p.364-375
Main Authors: Taira, R.K., Bashyam, V., Kangarloo, H.
Format: Article
Language:English
Subjects:
Calibration
Chemistry
Energy states
Information Storage and Retrieval - methods
Information systems
Information Theory
Knowledge representation
Linguistics
Medical
Medical Informatics - methods
Medical Records Systems, Computerized
Natural Language Processing
natural language processing (NLP)
Parsers
Physics
Potential energy
Psychology
Radiology
Sentences
Statistics
structured medical reporting
System testing
Terminology as Topic
Texts
Vocabulary, Controlled
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Summary:A parser for medical free text reports has been developed that is based on a chemistry/physics inspired ldquofield theoryrdquo for word-word sentence-level dependencies. The transition from the linguistic world to the world of interacting particles with potential energies is guided by a psycholinguistics thought experiment related to the amount of ldquoworkrdquo required to bring a reference word into an anchored configuration of words. Calibration experiments involving four and five grams were conducted. Data from these experiments were used as a knowledge source for estimating field conditions for words in sentences sampled from a corpus of medical reports. The result of the parser is a dependency tree that represents the global minimum energy state of the system of words for a given sentence. The system was trained and tested on a corpus of radiology reports. Preliminary performance, as quantified by link recall and precision statistics, is 84.9% and 89.9%, respectively.
ISSN:1089-7771
2168-2194
1558-0032
2168-2208
DOI:10.1109/TITB.2006.884368