Predicting three-year kidney graft survival in recipients with systemic lupus erythematosus.

Predicting the outcome of kidney transplantation is important in optimizing transplantation parameters and modifying factors related to the recipient, donor, and transplant procedure. As patients with end-stage renal disease (ESRD) secondary to lupus nephropathy are generally younger than the typical ESRD patients and also seem to have inferior transplant outcome, developing an outcome prediction model in this patient category has high clinical relevance. The goal of this study was to compare methods of building prediction models of kidney transplant outcome that potentially can be useful for clinical decision support.

Nursing informatics competencies required of nurses in Taiwan.

Purpose: In today's workplace, nurses are highly skilled professionals possessing expertise in both information technology and nursing. Nursing informatics competencies are recognized as an important capability of nurses. No established guidelines existed for nurses in Asia.

Predicting the start week of respiratory syncytial virus outbreaks using real time weather variables.

Background: Respiratory Syncytial Virus (RSV), a major cause of bronchiolitis, has a large impact on the census of pediatric hospitals during outbreak seasons. Reliable prediction of the week these outbreaks will start, based on readily available data, could help pediatric hospitals better prepare for large outbreaks.

Methods: Naïve Bayes (NB) classifier models were constructed using weather data from 1985-2008 considering only variables that are available in real time and that could be used to forecast the week in which an RSV outbreak will occur in Salt Lake County, Utah.

Specialist discrimination of toxic exposure severity at a poison control center.

Background: Perceived severity has been shown to affect decision-making processes in telephone triage. However, the accuracy of specialists in poison information's (SPIs') perceptions of severity of poison exposures is unknown.

Objective: The purpose of this study was to describe the ability of SPIs to predict severity of medical outcome on the basis of the information obtained during the initial poison control center's phone call.

Building knowledge for poison control: the novel pairing of communication analysis with data mining methods.

As information systems become increasingly integrated with health care delivery, vast amounts of clinical data are stored. Knowledge discovery and data mining methods are potentially powerful for the induction of knowledge models from this data relevant to nursing outcomes. However, an important barrier to the widespread application of these methods for induction of nursing knowledge models is that important concepts relevant to nursing outcomes are often unrepresented in clinical data.

Patient-level analysis of outcomes using structured labor and delivery data.

This paper presents methods for identifying and analyzing associations among nursing care processes, patient attributes, and patient outcomes using unit-level and patient-level representations of care derived from computerized nurse documentation. The retrospective, descriptive analysis included documented nursing events for 900 Labor and Delivery patients at three hospitals over the 2-month period of January and February 2006. Two models were used to produce quantified measurements of nursing care received by each patient.

Forecasting hospital census at a tertiary care children's hospital.

Developing a forecasting tool for patient census allows for improved staffing, better resource utilization and mobilization, and improved timing of educational campaigns around the disease control process. Using a neural network approach we evaluated several different models and variables for predicting patient census prospectively. These initial studies enabled selection of a subset of predictor variables and show that different network models, and variables must be used based on the season.

Modeling the distribution of Nursing Effort using structured Labor and Delivery documentation.

Our study objectives included the development and evaluation of models for representing the distribution of shared unit-wide nursing care resources among individual Labor and Delivery patients using quantified measurements of nursing care, referred to as Nursing Effort. The models were intended to enable discrimination between the amounts of care delivered to patient subsets defined by attributes such as patient acuity. For each of five proposed models, scores were generated using an analysis set of 686,402 computerized nurse-documented events associated with 1093 patients at three hospitals during January and February 2006.

Nurse residency program implementation: the Utah experience.

Recently, nurse residency programs have been shown to improve satisfaction and enhance the retention of new graduate nurses, offering one solution for hospital executives, administrators, and managers searching for innovative ways to address nursing staff shortages. This article identifies crucial lessons that will assist leaders in designing and implementing a nurse residency program in their own institutions. The lessons are drawn from the experience of the successful University of Utah program.

Classification of smoking cessation status with a backpropagation neural network.

This study examined the ability of a backpropagation neural network (BPNN) classifier to distinguish between current and former smokers in the 2000 National Health Interview Survey (NHIS) sample adult file. The BPNN classifier performance exceeded that of random chance, with asymmetric 95% confidence intervals for A(z) (area under receiver operating characteristic curve)=(0.7532, 0.