Diana M. Negoescu

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Sequencing Experiments in Drug Discovery Diana M. Negoescu Department of Management Science and Engineering, Stanford University, Stanford, CA 94305, USA, Peter I. Frazier School of Operations Research and Information Engineering, Cornell University, Ithaca, NY 14853, USA, Warren B. Powell Department of Operations Research and Financial Engineering(More)
BACKGROUND Prisons of the former Soviet Union (FSU) have high rates of multidrug-resistant tuberculosis (MDR-TB) and are thought to drive general population tuberculosis (TB) epidemics. Effective prison case detection, though employing more expensive technologies, may reduce long-term treatment costs and slow MDR-TB transmission. METHODS AND FINDINGS We(More)
In this paper, we consider a version of the newsvendor problem in which the demand for newspapers is unknown and the lost sales are unobservable. This problem serves as a surrogate for more complex supply chain problems in which learning plays a role. We propose the knowledge gradient (KG) policy, which considers how much is learned about the demand(More)
We developed a mathematical model to identify the timing of antiretroviral therapy (ART) initiation that optimizes patient outcomes as a function of patient CD4 count, age, cardiac mortality risk, sex, and personal preferences. Our goal was to find the conditions that maximize patient quality-adjusted life expectancy (QALE) in the context of our model.(More)
We present a new technique for adaptively choosing the sequence of molecular compounds to test in drug discovery. Beginning with a base compound, we consider the problem of searching for a chemical derivative of this molecule that best treats a given disease. The problem of choosing the molecules to test to maximize the expected quality of the best compound(More)
Currently available medication for treating many chronic diseases is often effective only for a subgroup of patients, and biomarkers accurately assessing whether an individual belongs to this subgroup do not exist. In such settings, physicians learn about the effectiveness of a drug primarily through experimentation, i.e., by initiating treatment and(More)
Background. Viral load (VL) monitoring for patients receiving antiretroviral therapy (ART) is recommended worldwide. However, the costs of frequent monitoring are a barrier to implementation in resource-limited settings. The extent to which personalized monitoring frequencies may be cost-effective is unknown. Methods. We created a simulation model(More)
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