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OBJECTIVES To develop a risk-adjustment methodology that maximizes the use of automated physiology and diagnosis data from the time period preceding hospitalization. DESIGN : Retrospective cohort study using split-validation and logistic regression. SETTING Seventeen hospitals in a large integrated health care delivery system. SUBJECTS Patients (n =(More)
We review three leading stochastic optimization methods—simulated annealing, genetic algorithms, and tabu search. In each case we analyze the method, give the exact algorithm, detail advantages and disadvantages, and summarize the literature on optimal values of the inputs. As a motivating example we describe the solution—using Bayesian decision theory, via(More)
We use simulation studies, whose design is realistic for educational and medical research (as well as other fields of inquiry), to compare Bayesian and likelihood-based methods for fitting variance-components (VC) and random-effects logistic regression (RELR) models. The likelihood (and approximate likelihood) approaches we examine are based on the methods(More)
Prior research on pair programming has found that compared to students who work alone, students who pair have shown increased confidence in their work, greater success in CS1, and greater retention in computer-related majors. In these earlier studies, pairing and solo students were not given the same programming assignments. This paper reports on a study in(More)
OBJECTIVE Using a comprehensive inpatient electronic medical record, we sought to develop a risk-adjustment methodology applicable to all hospitalized patients. Further, we assessed the impact of specific data elements on model discrimination, explanatory power, calibration, integrated discrimination improvement, net reclassification improvement,(More)
BACKGROUND Ward patients who experience unplanned transfer to intensive care units have excess morbidity and mortality. OBJECTIVE To develop a predictive model for prediction of unplanned transfer from the medical-surgical ward to intensive care (or death on the ward in a patient who was "full code") using data from a comprehensive inpatient electronic(More)
Hierarchical models (HMs; Lindley & Smith, 1972) ooer considerable promise to increase the level of realism in social science modeling, but the scope of what is validly concludable with them is limited, and recent technical advances in allied elds may not yet have been put to best use in implementing them. In this paper I (1) examine three levels of(More)
OBJECTIVE To define a quantitative stratification algorithm for the risk of early-onset sepsis (EOS) in newborns ≥ 34 weeks' gestation. METHODS We conducted a retrospective nested case-control study that used split validation. Data collected on each infant included sepsis risk at birth based on objective maternal factors, demographics, specific clinical(More)