Gaydar: a social-cognitive analysis.

Gaydar is a popular culture term that refers to the ability for one individual to correctly identify the sexual orientation of another. Recent studies have documented a phenomenon that mirrors gaydar. The purpose of this paper is to provide a cognitive analysis of the "gaydar" phenomenon.

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.

Nursing collaboratory development via the Internet.

Old paradigms of research need to change as funding becomes more competitive and less available and as institutions demand more productivity of research scholars while providing decreasing levels of support. Development of a Nursing Collaboratory provides an opportunity for nurse researchers to communicate and collaborate (using the Internet) during phases of the research process. A proposed model of Nursing Collaboratory development is described, and opportunities and issues are discussed.

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.

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).

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.

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.

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.

Expert nurses and expert systems. Research and development issues.

Expert systems in nursing have developed primarily through deductive, theoretical approaches and have, for the most part, failed to articulate the need for expert clinical nurse knowledge and heuristics as a basis for developing expert systems. Research using knowledge engineering and nurse experts, as well as multi-method approaches to researching nurse experts, should provide content for development of computer systems, in general, and expert systems, in particular, in nursing.

Self-care for the obstetrical patient. A nursing framework.

In using Orem's self-care framework, the nurse operates within a client-centered system which encourages the individual to be responsible for her own care. The philosophy changes from "I am here to take care of you" (maternalistic) to "I am here to assist you in taking care of yourself" (helping relationship). Even very ill or disabled persons (or their families) can be included in decision-making processes when actual self-care is not possible.