Abstract
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 second phase, regularization, standardizes the terms in the initial target structure via a compositional mapping of multi-word phrases. The third phase, encoding, maps the terms to a controlled vocabulary. Radiology is the test domain for the processor and the target structure is a formal model for representing clinical information in that domain. MEASUREMENTS: The impression sections of 230 radiology reports were encoded by the processor. Results of an automated query of the resultant database for the occurrences of four diseases were compared with the analysis of a panel of three physicians to determine recall and precision. RESULTS: Without training specific to the four diseases, recall and precision of the system (combined effect of the processor and query generator) were 70% and 87%. Training of the query component increased recall to 85% without changing precision.
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Selected References
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- Baud R. H., Rassinoux A. M., Scherrer J. R. Natural language processing and semantical representation of medical texts. Methods Inf Med. 1992 Jun;31(2):117–125. [PubMed] [Google Scholar]
- Bell D. S., Greenes R. A., Doubilet P. Form-based clinical input from a structured vocabulary: initial application in ultrasound reporting. Proc Annu Symp Comput Appl Med Care. 1992:789–790. [PMC free article] [PubMed] [Google Scholar]
- Benoit R. G., Cushing B. M., Teitelbaum S. D., van Wijngaarden M. H., Canfield K. Direct physician entry of injury information and automated coding via a graphical user interface. Proc Annu Symp Comput Appl Med Care. 1992:787–788. [PMC free article] [PubMed] [Google Scholar]
- Cristea D., Mihaescu T. Combining menus with natural language processing in recording medical data. J Clin Comput. 1988;16(5-6):156–166. [PubMed] [Google Scholar]
- Grams R. R., Jin Z. M. The natural language processing of medical databases. J Med Syst. 1989 Apr;13(2):79–87. doi: 10.1007/BF00999245. [DOI] [PubMed] [Google Scholar]
- Johnson K., Poon A., Shiffman S., Lin R., Fagan L. A history-taking system that uses continuous speech recognition. Proc Annu Symp Comput Appl Med Care. 1992:757–761. [PMC free article] [PubMed] [Google Scholar]
- Lin R., Lenert L., Middleton B., Shiffman S. A free-text processing system to capture physical findings: Canonical Phrase Identification System (CAPIS). Proc Annu Symp Comput Appl Med Care. 1991:168–172. [PMC free article] [PubMed] [Google Scholar]
- Lin R., Lenert L., Middleton B., Shiffman S. A free-text processing system to capture physical findings: Canonical Phrase Identification System (CAPIS). Proc Annu Symp Comput Appl Med Care. 1991:843–847. [PMC free article] [PubMed] [Google Scholar]
- Linn N. A., Rubenstein R. M., Bowler A. E., Dixon J. L. Improving the quality of emergency department documentation using the voice-activated word processor: interim results. Proc Annu Symp Comput Appl Med Care. 1992:772–776. [PMC free article] [PubMed] [Google Scholar]
- Shiffman S., Lane C. D., Johnson K. B., Fagan L. M. The integration of a continuous-speech-recognition system with the QMR diagnostic program. Proc Annu Symp Comput Appl Med Care. 1992:767–771. [PMC free article] [PubMed] [Google Scholar]
- Zingmond D., Lenert L. A. Monitoring free-text data using medical language processing. Comput Biomed Res. 1993 Oct;26(5):467–481. doi: 10.1006/cbmr.1993.1033. [DOI] [PubMed] [Google Scholar]