Abstract
OBJECTIVE: Development of methods for building concept models to support structured data entry and image retrieval in chest radiography. DESIGN: An organizing model for chest-radiographic reporting was built by analyzing manually a set of natural-language chest-radiograph reports. During model building, clinician-informaticians judged alternative conceptual structures according to four criteria: content of clinically relevant detail, provision for semantic constraints, provision for canonical forms, and simplicity. The organizing model was applied in representing three sample reports in their entirety. To explore the potential for automatic model discovery, the representation of one sample report was compared with the noun phrases derived from the same report by the CLARIT natural-language processing system. RESULTS: The organizing model for chest-radiographic reporting consists of 62 concept types and 17 relations, arranged in an inheritance network. The broadest types in the model include finding, anatomic locus, procedure, attribute, and status. Diagnoses are modeled as a subtype of finding. Representing three sample reports in their entirety added 79 narrower concept types. Some CLARIT noun phrases suggested valid associations among subtypes of finding, status, and anatomic locus. CONCLUSIONS: A manual modeling process utilizing explicitly stated criteria for making modeling decisions produced an organizing model that showed consistency in early testing. A combination of top-down and bottom-up modeling was required. Natural-language processing may inform model building, but algorithms that would replace manual modeling were not discovered. Further progress in modeling will require methods for objective model evaluation and tools for formalizing the model-building process.
Full Text
The Full Text of this article is available as a PDF (1.4 MB).
Selected References
These references are in PubMed. This may not be the complete list of references from this article.
- Barnett G. O., Cimino J. J., Hupp J. A., Hoffer E. P. DXplain. An evolving diagnostic decision-support system. JAMA. 1987 Jul 3;258(1):67–74. doi: 10.1001/jama.258.1.67. [DOI] [PubMed] [Google Scholar]
- Baud R., Lovis C., Alpay L., Rassinoux A. M., Scherrer J. R., Nowlan A., Rector A. Modelling for natural language understanding. Proc Annu Symp Comput Appl Med Care. 1993:289–293. [PMC free article] [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]
- Bernauer J. Conceptual graphs as an operational model for descriptive findings. Proc Annu Symp Comput Appl Med Care. 1991:214–218. [PMC free article] [PubMed] [Google Scholar]
- Cimino J. J., Clayton P. D., Hripcsak G., Johnson S. B. Knowledge-based approaches to the maintenance of a large controlled medical terminology. J Am Med Inform Assoc. 1994 Jan-Feb;1(1):35–50. doi: 10.1136/jamia.1994.95236135. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Evans D. A., Hersh W. R., Monarch I. A., Lefferts R. G., Handerson S. K. Automatic indexing of abstracts via natural-language processing using a simple thesaurus. Med Decis Making. 1991 Oct-Dec;11(4 Suppl):S108–S115. [PubMed] [Google Scholar]
- Feinstein A. R. ICD, POR, and DRG. Unsolved scientific problems in the nosology of clinical medicine. Arch Intern Med. 1988 Oct;148(10):2269–2274. doi: 10.1001/archinte.148.10.2269. [DOI] [PubMed] [Google Scholar]
- Greenes R. A., Barnett G. O., Klein S. W., Robbins A., Prior R. E. Recording, retrieval and review of medical data by physician-computer interaction. N Engl J Med. 1970 Feb 5;282(6):307–315. doi: 10.1056/NEJM197002052820605. [DOI] [PubMed] [Google Scholar]
- Greenes R. A., McClure R. C., Pattison-Gordon E., Sato L. The findings--diagnosis continuum: implications for image descriptions and clinical databases. Proc Annu Symp Comput Appl Med Care. 1992:383–387. [PMC free article] [PubMed] [Google Scholar]
- Kuhn K., Gaus W., Wechsler J. G., Janowitz P., Tudyka J., Kratzer W., Swobodnik W., Ditschuneit H. Structured reporting of medical findings: evaluation of a system in gastroenterology. Methods Inf Med. 1992 Nov;31(4):268–274. [PubMed] [Google Scholar]
- Leeming B. W., Simon M., Jackson J. D., Horowitz G. L., Bleich H. L. Advances in radiologic reporting with Computerized Language Information Processing (CLIP). Radiology. 1979 Nov;133(2):349–353. doi: 10.1148/133.2.349. [DOI] [PubMed] [Google Scholar]
- Lussier Y. A., Maksud M., Desruisseaux B., Yale P. P., St-Arneault R. PureMD: a Computerized Patient Record software for direct data entry by physicians using a keyboard-free pen-based portable computer. Proc Annu Symp Comput Appl Med Care. 1992:261–264. [PMC free article] [PubMed] [Google Scholar]
- Masarie F. E., Jr, Miller R. A., Bouhaddou O., Giuse N. B., Warner H. R. An interlingua for electronic interchange of medical information: using frames to map between clinical vocabularies. Comput Biomed Res. 1991 Aug;24(4):379–400. doi: 10.1016/0010-4809(91)90035-u. [DOI] [PubMed] [Google Scholar]
- Naeymi-Rad F., Almeida F. D., Trace D. IMR-entry (Intelligent Medical Record-Entry). Proc Annu Symp Comput Appl Med Care. 1992:783–784. [PMC free article] [PubMed] [Google Scholar]
- Rector A. L., Nowlan W. A., Kay S. Foundations for an electronic medical record. Methods Inf Med. 1991 Aug;30(3):179–186. [PubMed] [Google Scholar]
- Volot F., Zweigenbaum P., Bachimont B., Ben Said M., Bouaud J., Fieschi M., Boisvieux J. F. Structuration and acquisition of medical knowledge. Using UMLS in the conceptual graph formalism. Proc Annu Symp Comput Appl Med Care. 1993:710–714. [PMC free article] [PubMed] [Google Scholar]