Using Artificial Intelligence for Model Selection

We apply the optimization algorithm Adaptive Simulated Annealing (ASA) to the problem of analyzing data on a large population and selecting the best model to predict that an individual with various traits will have a particular disease. We compare ASA with traditional forward and backward regression on computer simulated data. We find that the traditional methods of modeling are better for smaller data sets whereas a numerically stable ASA seems to perform better on larger and more complicated data sets.Comment: 10 pages, no figures, in Proceedings, Hawaii International Conference on Statistics and Related Fields, June 5-8, 200

Public Opinion in Perspective: Wisconsin's Mind on Education

Outlines survey findings on Wisconsin residents' views on the quality of public schools and reforms including increased spending, accountability, vouchers, charter schools, online education, and merit pay, compared with Milwaukee and national surveys

Fitting multilevel multivariate models with missing data in responses and covariates that may include interactions and non-linear terms

The paper extends existing models for multilevel multivariate data with mixed response types to handle quite general types and patterns of missing data values in a wide range of multilevel generalized linear models. It proposes an efficient Bayesian modelling approach that allows missing values in covariates, including models where there are interactions or other functions of covariates such as polynomials. The procedure can also be used to produce multiply imputed complete data sets. A simulation study is presented as well as the analysis of a longitudinal data set. The paper also shows how existing multiprocess models for handling endogeneity can be extended by the framework proposed

National Aeronautics and Space Administration (NASA)/American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program, 1989, volume 1

The 1989 Johnson Space Center (JSC) National Aeronautics and Space Administration (NASA)/American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program was conducted by Texas A and M University and JSC. The 10-week program was operated under the auspices of the ASEE. The program at JSC, as well as the programs at other NASA Centers, was funded by the Office of University Affairs, NASA Headquarters, Washington, D.C. The objectives of the program, which began nationally in 1964 and at JSC in 1965, are: (1) to further the professional knowledge of qualified engineering and science faculty members; (2) to stimulate an exchange of ideas between participants and NASA; (3) to enrich and refresh the research and teaching activities of participants' institutions; and (4) to contribute to the research objective of the NASA Centers

Risk Prediction In Older Adults After Acute Myocardial Infarction: The Silver-Ami Study

Older adults are at risk for functional decline after hospitalization for acute myocardial infarction (AMI). Our goal with this thesis is to explore two outcomes relevant to maintenance of physical function, falls and cardiac rehabilitation (CR) utilization in a cohort of adults over the age of 75 hospitalized with acute myocardial infarction. We aim to describe the risk of falls within six months of discharge and the rates of CR use, and to identify factors associated with these outcomes. Our project uses data from the SILVER-AMI study, a prospectively designed cohort study which enrolled 3000 patients over the age of 75 hospitalized with acute myocardial infarction and followed them for six months after discharge. Extensive baseline data was collected on demographics, clinical and psychosocial factors, and geriatric impairments. Outcome data on falls was collected at six months via medical record adjudication and survey, and on CR use by survey. 557 (21.6%) of 2584 participants reported at least one fall within six months of discharge. Independent predictors after logistic regression analysis included: impaired functional mobility (OR 1.5 [1.07-2.11]), recent fall history (OR 2.97 [2.37- 3.74]), longer length of stay (OR 1.04 [1.02-1.07] per day, visual impairment (OR 1.33 [1.08-1.64]), and weak grip strength (OR 1.28 [1.02-1.60]). 192 (6.4%) of 3006 participants were found to have a medically serious fall within six months of discharge. Independent predictors of medically serious falls after logistic regression analysis included: impaired functional mobility (OR 1.85 [1.11-3.09]), recent fall history (OR 1.73 [1.23-2.42]), longer length of stay (OR 1.03 [1.01-1.06] per day, living alone (OR 1.37 [1.00-1.87, p = 0.048]), and impairment in the bathing ADL (OR 1.74 [1.06-2.86]). 943 (39.5%) of 2387 participants reported participating in CR within six months of discharge. Independent predictors of CR use after logistic regression analysis included: older age (OR 0.97 [0.95-0.99] per year), non-white race (OR 0.69 [0.50-0.97]), having less than 12 years of education (OR 0.71 [0.59-0.85]), receiving percutaneous (OR 2.07 [1.66-2.57]) or surgical (OR 4.70 [3.32-6.67]) revascularization, cognitive impairment (OR 0.58 [0.43-0.78]), and living alone (OR 0.77 [0.64-0.93]). From these results, we conclude that falls and CR underutilization are important problems facing older adults after AMI. The comprehensive geriatric assessment performed in SILVER-AMI highlighted independent robust predictors of both functional outcomes. This indicates that there is a role for assessing geriatric impairments during an AMI hospitalization, as identifying patients at risk for poor functional outcomes can lead to steps toward improving their care. High fall risk could be a reason to avoid anticoagulant therapy. Identifying patients less likely to attend CR can allow development of interventions to close this gap in care