This project seeks to develop a natural language understanding system specifically aimed at extracting relevant clinical facts from medical reports. The system is based largely on a semantic parsing technique that stresses the use of medical knowledge encoded in four forms. These forms include: a hierarchy of terms embedded in a general purpose medical data dictionary; a semantic network designed to capture knowledge concerning the relative locations of different anatomic sites; a collection of frames specifying allowable combinations of terms. These frames also have a hierarchial organization designed to help the parser find an appropriate format for the recognition and storage of a complex medical fact; a transformational grammar attached to the hierarchy of frames which can propose the different ways a medical fact, as indicated by the combined terms in a frame, might be expressed; a causal network developed specifically to allow disambiguation of the many incompletely expressed facts that can be found in a medical report. Both a lexicon expressing the different words known to the system and a thesaurus expressing all meaningful phrases expected in the reporting domain will also be built. A system that uses this information to parse medical text will be constructed and evaluated. The domains tested will be the reports of chest x-rays and admitting history and physical examination for patients with pulmonary and/or cardiac diseases. The evaluation will determine whether relevant medical facts presented in the reports are captured and stored by the natural language parser in an integrated, general purpose medical data base. The goal of this project is to further techniques that allow the encoding of medical information captured as free text into a form appropriate for research, quality, assurance, and direct clinical decision support.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Research Project (R01)
Project #
5R01LM005323-02
Application #
3374328
Study Section
Biomedical Library and Informatics Review Committee (BLR)
Project Start
1991-08-01
Project End
1994-07-31
Budget Start
1992-08-01
Budget End
1993-07-31
Support Year
2
Fiscal Year
1992
Total Cost
Indirect Cost
Name
Lds Hospital
Department
Type
DUNS #
City
Salt Lake City
State
UT
Country
United States
Zip Code
84143
Haug, P J; Christensen, L; Gundersen, M et al. (1997) A natural language parsing system for encoding admitting diagnoses. Proc AMIA Annu Fall Symp :814-8
Gundersen, M L; Haug, P J; Pryor, T A et al. (1996) Development and evaluation of a computerized admission diagnoses encoding system. Comput Biomed Res 29:351-72
Haug, P J; Koehler, S; Lau, L M et al. (1995) Experience with a mixed semantic/syntactic parser. Proc Annu Symp Comput Appl Med Care :284-8
Li, Y C; Haug, P J; Warner, H R (1994) Automated transformation of probabilistic knowledge for a medical diagnostic system. Proc Annu Symp Comput Appl Med Care :765-9
Haug, P; Koehler, S; Lau, L M et al. (1994) A natural language understanding system combining syntactic and semantic techniques. Proc Annu Symp Comput Appl Med Care :247-51
Li, Y C; Haug, P J (1993) Evaluating the quality of a probabilistic diagnostic system using different inferencing strategies. Proc Annu Symp Comput Appl Med Care :471-7
Haug, P J (1993) Uses of diagnostic expert systems in clinical care. Proc Annu Symp Comput Appl Med Care :379-83