Research Paper: A General Natural-language Text Processor for Clinical Radiology

@article{Friedman1994ResearchPA,
  title={Research Paper: A General Natural-language Text Processor for Clinical Radiology},
  author={Carol Friedman and Philip O. Alderson and John H. M. Austin and James J. Cimino and Stephen B. Johnson},
  journal={Journal of the American Medical Informatics Association : JAMIA},
  year={1994},
  volume={1 2},
  pages={
          161-74
        }
}
OBJECTIVE Development of a general natural-language processor that identifies clinical information in narrative reports and maps that information into a structured representation containing clinical terms. DESIGN The natural-language processor provides three phases of processing, all of which are driven by different knowledge sources. The first phase performs the parsing. It identifies the structure of the text through use of a grammar that defines semantic patterns and a target form. The… 
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References

SHOWING 1-9 OF 9 REFERENCES
Natural language processing and semantical representation of medical texts.
TLDR
The components of an NLP system, which is currently being developed in the Geneva Hospital, and within the European Community's AIM programme, are described, which includes a Natural Language Analyser, a Conceptual Graphs Builder, a Data Base Storage component, a Query Processor, a Natural language Generator and a Translator.
Monitoring free-text data using medical language processing.
TLDR
Evaluated the RadTRAC (Radiology Text Report Analyzer and Classifier) system, which uses a medical language processing tool and rules derived from statistical analysis of a database to process free-text chest X-ray (CXR) reports and identify reports that describe new or expanding neoplasms for the purpose of monitoring the follow-up of patients.
A free-text processing system to capture physical findings: Canonical Phrase Identification System (CAPIS).
TLDR
A prototype system for extracting physical examination findings from dictated admission summaries is described, using a concept-based free-text processing algorithm that identifies user-selected targetphysical examination findings to enrich an existing clinical database.
The integration of a continuous-speech-recognition system with the QMR diagnostic program.
TLDR
A method for matching spoken findings names expressed in natural language to QMR terms is presented, based on a semantic representation of findings that both minimize the effect of misrecognition and derive grammars that are necessary for supporting the recognition process.
Form-based clinical input from a structured vocabulary: initial application in ultrasound reporting.
TLDR
Ultra-STAR (Ultrasound Structured Attribute Reporting) allows a sonographer to record reports using a hierarchic standardized vocabulary using a graphical user interface to select concepts from the vocabulary.
A history-taking system that uses continuous speech recognition.
TLDR
Q-MED is an automated history-taking system that uses speaker-independent continuous speech as its main interface modality and an evaluation of the natural language parser showed an overall semantic accuracy of 87 percent.
Direct physician entry of injury information and automated coding via a graphical user interface.
TLDR
A graphical, anatomic-based interface for quick collection of detailed injury information directly from the trauma physician, using SNOMED III nomenclature to create and store ICD, AIS, and trauma registry codes for each injury is tested.
Improving the quality of emergency department documentation using the voice-activated word processor: interim results.
TLDR
Based on preliminary assessments, the keys to successful use appear to include physician and group commitment, acceptance of a steep learning curve, and flexibility in adapting the computer software and/or practice habits.