Biomedical subjects

R A Bielefeld

Publications and source records attributed to R A Bielefeld.

4 recordsLinked to original sources

PubMed1996

Storing sparse and repeated data in multivariate Markovian models of tuberculosis spread.

Through the use of appropriate sparse storage techniques, we were able to reduce memory usage in a multivariate Markovian model for the spread of tuberculosis in the United States through the year 2010. A straightforward software implementation of the model would have required approximately 2.5 x 10(9) bytes of storage for the population of each year being modeled and approximately 1.3 x 10(14) bytes of storage for each year-to-year set of transition probabilities. We were able to reduce memory usage in the model by 96% for cross-sectional population data and over 99.9% for transition probability data. Data structure initialization time for population data was increased by a factor of 16.48 and lookup time for population data was increased by a factor of 11.3 over times required for an array implementation. For transition data the initialization and lookup times were increased by negligible factors. This work was done under contract from the Centers for Disease Control and the Association of Teachers of Preventive Medicine.

Centers for Disease Control and Prevention, U.S.↗

PubMed1995

A neural network approach for the determination of interhospital transport mode.

We report on the construction of neural networks for determining whether pediatric patients requiring transport to a tertiary care center should be moved by air or by ground. The networks were based on the functional-link net architecture. In two experiments, feedforward supervised-learning neural nets were trained with examples of an expert's decisions and then were used in a consulting mode to provide advice on cases not previously encountered. Training and validation were performed by a combination of the k-fold cross-validation and leaving-one-out sampling methods. Use of the functional-link net rather than the customary backpropagation net enabled us to carry out the training with fairly large amounts of data in realistically short time periods. In the first experiment, capillary refill, skin color, and stridor were consistently the input variables that were most strongly associated with the decision output. In both experiments, the networks were validated by comparing their performance retrospectively against the determination of an expert pediatric transport physician. The network was trained based on the expert's opinion about the correct mode of transport for each case with error rates of less than 10(-5).

Artificial Intelligence↗

PubMedDate not supplied

Multivariate Markovian modeling of tuberculosis: forecast for the United States.

We have developed a computer-implemented, multivariate Markov chain model to project tuberculosis (TB) incidence in the United States from 1980 to 2010 in disaggregated demographic groups. Uncertainty in model parameters and in the projections is represented by fuzzy numbers. Projections are made under the assumption that current TB control measures will remain unchanged for the projection period. The projections of the model demonstrate an intermediate increase in national TB incidence (similar to that which actually occurred) followed by continuing decline. The rate of decline depends strongly on geographic, racial, and ethnic characteristics. The model predicts that the rate of decline in the number of cases among Hispanics will be slower than among white non-Hispanics and black non-Hispanics a prediction supported by the most recent data.

Adolescent↗

PubMedDate not supplied

A research database for improved data management and analysis in longitudinal studies.

We developed a research database for a five-year prospective investigation of the medical, social, and developmental correlates of chronic lung disease during the first three years of life. We used the Ingres database management system and the Statit statistical software package. The database includes records containing 1300 variables each, the results of 35 psychological tests, each repeated five times (providing longitudinal data on the child, the parents, and behavioral interactions), both raw and calculated variables, and both missing and deferred values. The four-layer menu-driven user interface incorporates automatic activation of complex functions to handle data verification, missing and deferred values, static and dynamic backup, determination of calculated values, display of database status, reports, bulk data extraction, and statistical analysis.

Bronchopulmonary Dysplasia↗