KNOWLEDGE ACQUISITION FOR ASSESSMENT OF PRETERM LABOR IN PREGNANT WOMEN (ARTIFICIAL INTELLIGENCE, LABOR).
Contributors: University of Kansas.
Published: 1992
Number of pages: 233
Subjects: Health Sciences, Nursing.; Health Sciences, Obstetrics and Gynecology.; Computer Science.
Description: The purpose of this study was to develop and describe a knowledge base for nurses' assessment of preterm labor risk. Review of the literature provided both theoretical and empirical support to generate and test a knowledge base of preterm labor risk. Despite the fact that existing preterm risk screening tools do not meet psychometric standards for reliability and validity, these tools are used on a daily basis to intervene with pregnant women.

The first phase of this study acquired data (52 megabytes) for 2739 randomly selected records of high risk pregnant women whose data were collected by perinatal nurses and stored in a computerized database. Subsequent knowledge acquisition methods designed for this study were pioneering work in the field of nursing informatics. The second phase of knowledge acquisition transformed data to information using exploratory factor analysis, multiple regression, and a machine learning algorithm, named 'ID3', to generate production rules directly from the data. The third phase of knowledge acquisition transformed information-to-knowledge using a panel of nurse experts to conduct content validity procedures in verifying production rules and providing lists of missing data and missing rules.

Results of this study found the majority of the 2739 subjects were caucasian (68%), 20 or more years of age (82%), high school or college educated (72%), married (80%), and non-smokers (87%). Exploratory factor analysis with 42 variables yielded 18 factors that accounted for 72.5% of the variance in weeks gestation at referral for preterm labor. Multiple regression found low correlations between 50 predictors and the criterion variable that rendered most results not meaningful for clinical practice. Cervical changes (r =.315) accounted for 8.7% of the variance (p $>$.0000) adding support for previous studies that found cervical changes predictive of preterm labor risk.

A software program with an ID3 machine learning algorithm generated 88 production rules directly from the data. However the software had limitations and performed poorly and the rules were found meaningless by a panel of experts. Experts generated a list of 40 data elements and 21 heuristic rules for future knowledge base development.
Notes: Source: Dissertation Abstracts International, Volume: 54-04, Section: B, page: 1897.

Thesis (PH.D.)--UNIVERSITY OF KANSAS, 1992.

School code: 0099.