The use of machine learning program LERS-LB 2.5 in knowledge acquisition for expert system development in nursing.
LERS-LB (Learning from Examples using Rough Sets Lower Boundaries) is a computer program based on rough set theory for knowledge acquisition, which extracts patterns from real-world data in generating production rules for expert system development. From LERS-LB evaluation of an SPSS-X data file containing data for recovery room patients, it was concluded that both statistical data files and existing databases can be converted to decision-table format needed by LERS-LB, but it is less desirable to work with statistical files than a well-developed database. It was also concluded that choosing a well-developed database and checking it thoroughly for accuracy and completeness should be done before running LERS-LB, or other learning programs, to avoid problems with data errors. Using rough set theory and a technique called 'dropping conditions' LERS-LB offers, at least in theory, a possible method for identifying which data items are critical to nursing practice. Further research and continued LERS-LB program enhancements still may help with identifying critical data items versus redundant data for nursing practice. LERS-LB, and other learning programs, offer techniques which will help reduce the knowledge acquisition bottleneck in nursing expert system development. It is doubtful, however, that learning programs will eliminate the need for involving domain experts in evaluating rules and expert systems for clinical decision support.