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Biomedical subjects

L K Woolery

Publications and source records attributed to L K Woolery.

7 recordsLinked to original sources
PubMed1995

Machine learning for development of an expert system to predict premature birth.

Normal pregnancy involves a term of 40 weeks gestation. Problems associated with low birthweight and prematurity continue to plague childbearing families and the healthcare system because 8-12% of all newborns in the United States deliver prior to 37 weeks gestation. The high cost of caring for premature babies increasingly treats all pregnant women as if they are 'high risk' for preterm birth. Artificial intelligence techniques used a machine learning program named LERS1 with large datasets (n = 18,890; 214 variables), statistical analysis, expert verification techniques, and a prototype expert system2 that yielded improved accuracy (53-90%) over existing manual techniques (17-38%) for predicting preterm birth.

Adult↗
PubMed1995

A demonstration of the virtual nursing college.

This demonstration will illustrate the operation of a virtual nursing college (VNC) through the Internet. The key concepts to be shown include: Distance and remote learning and teaching; Multi-site collaboration in teaching and clinical research using groupware; Multimedia courseware found in programmable virtual classrooms; Personal knowbots that manage information; Virtual clinics with virtual patients and simulated patients.

British Columbia↗
PubMed1994

Improving prediction of preterm birth using a new classification scheme and rule induction.

Prediction of preterm birth is a poorly understood domain. The existing manual methods of assessment of preterm birth are 17%-38% accurate. The machine learning system LERS was used for three different datasets about pregnant women. Rules induced by LERS were used in conjunction with a classification scheme of LERS, based on "bucket brigade algorithm" of genetic algorithms and enhanced by partial matching. The resulting prediction of preterm birth in new, unseen cases is much more accurate (68%-90%).

Algorithms↗
PubMed1990

Professional standards and ethical dilemmas in nursing information systems.

A significant challenge for the next decade is the design and implementation of nursing information systems that enhance patient outcomes and professional standards in nursing. Computer vendors have the potential to drive nursing practice by dictating what gets documented as nursing care. Ethical dilemmas related to appropriate roles for nurses in designing, developing, using, and validating computer systems need careful consideration. The author suggests ways nurse administrators can get involved in this important process.

Ethics, Nursing↗
PubMedDate not supplied

Machine learning for an expert system to predict preterm birth risk.

OBJECTIVE: Develop a prototype expert system for preterm birth risk assessment of pregnant women. Normal gestation involves a term of 40 weeks, but because 8-12% of the newborns in the United States are delivered prior to 37 weeks' gestation, problems associated with prematurity continue to plague individuals, families, and the health care system. DESIGN: A knowledge-base development methodology used machine learning, statistical analysis, and validation techniques to analyze three large datasets (18,890 subjects and 214 variables). The dependent (i.e., decision) variable studied was weeks of gestation at delivery, with dichotomous coding of preterm delivery (prior to 37 weeks) and full-term delivery (37+ weeks). RESULTS: Machine learning with a program named Learning from Examples using Rough Sets (LERS) induced 520 usable rules that were entered into a prototype expert system. The prototype expert system was 53-88% accurate in predicting preterm delivery for 9,419 patients. CONCLUSION: The prototype expert system was more accurate than traditional manual techniques in predicting preterm birth.

Adult↗