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Biomedical subjects

K W Bauer

Publications and source records attributed to K W Bauer.

3 recordsLinked to original sources

Use of statistical process control for surveillance of pulmonary dysfunction in groups in the workplace.

Workplace surveillance is an essential feature of an effective occupational health program. Unfortunately, many health care organizations are beginning to collect large quantities of clinical information without much thought to subsequent application. In this paper, we propose a screening technique to help manage this situation. Specifically, we advocate "control charts for fraction non-conforming" for use in the medical surveillance of work areas. A military installation in the mid-western United States with 63 work areas is analyzed using up to seven years worth of spirometry data. Based on the results of 6 separate tests, a classification of normal or abnormal was made for each individual and the percent of abnormalities by area and year was calculated. The results were analyzed via control charts and contrasted to a preset percentage method. Cigarette smoking was then controlled for in the analysis to account for abnormalities that may be occupationally related versus personal habits. We demonstrate the utility of control charts for the compact display of surveillance data and show how it can aid in the analysis of an extremely complex health care concern. A recommendation is made for managing a surveillance program using spirometry and control charts and supported by follow-on investigation of two areas flagged as abnormal by the procedure.

Adult↗

Selecting optimal experiments for multiple output multilayer perceptrons.

Where should a researcher conduct experiments to provide training data for a multilayer perceptron? This question is investigated, and a statistical method for selecting optimal experimental design points for multiple output multilayer perceptrons is introduced. Multiple class discrimination problems are examined using a framework in which the multilayer perceptron is viewed as a multivariate nonlinear regression model. Following a Bayesian formulation for the case where the variance-covariance matrix of the responses is unknown, a selection criterion is developed. This criterion is based on the volume of the joint confidence ellipsoid for the weights in a multilayer perceptron. An example is used to demonstrate the superiority of optimally selected design points over randomly chosen points, as well as points chosen in a grid pattern. Simplification of the basic criterion is offered through the use of Hadamard matrices to produce uncorrelated outputs.

Algorithms↗