ORCID Profile
0000-0001-8515-5877
Current Organisation
North Carolina State University
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Publisher: IEEE
Date: 12-12-2021
Publisher: Informa UK Limited
Date: 22-06-2023
Publisher: IEEE
Date: 12-2019
Publisher: Elsevier BV
Date: 10-2023
Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
Date: 03-2023
Abstract: Simulation optimization involves optimizing some objective function that can only be estimated via stochastic simulation. Many important problems can be profitably viewed within this framework. Whereas many solvers—implementations of simulation-optimization algorithms—exist or are in development, comparisons among solvers are not standardized and are often limited in scope. Such comparisons help advance solver development, clarify the relative performance of solvers, and identify classes of problems that defy efficient solution, among many other uses. We develop performance measures and plots, and estimators thereof, to evaluate and compare solvers and diagnose their strengths and weaknesses on a testbed of simulation-optimization problems. We explain the need for two-level simulation in this context and provide supporting convergence theory. We also describe how to use bootstrapping to obtain error estimates for the estimators. History: Accepted by Bruno Tuffin, area editor for simulation. Funding: This work was supported by the National Science Foundation [Grants CMMI-2035086, CMMI-2206972, and TRIPODS+X DMS-1839346]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplementary Information [ oi/suppl/10.1287/ijoc.2022.1261 ] or is available from the IJOC GitHub software repository ( github.com/INFORMSJoC ) at [ 0.5281/zenodo.7329235 ].
Publisher: Springer International Publishing
Date: 2022
Publisher: Cold Spring Harbor Laboratory
Date: 09-08-2023
DOI: 10.1101/2023.08.06.23293723
Abstract: Annually, urinary tract infections (UTIs) affect over a hundred million people worldwide. Early detection of high-risk in iduals can help prevent hospitalization for UTIs, which imposes significant economic and social burden on patients and caregivers. We present two methods to generate risk score models for unplanned UTI hospitalization. We utilize a s le of patients from the insurance claims data provided by the Centers for Medicare and Medicaid Services to develop and validate the proposed methods. Our dataset encompasses a wide range of features, such as demographics, medical history, and healthcare utilization of the patients along with provider quality metrics and community-based metrics. The proposed methods scale and round the coefficients of an underlying logistic regression model to create scoring tables. We present computational experiments to evaluate the prediction performance of both models. We also discuss different features of these models with respect to their impact on interpretability. Our findings emphasize the effectiveness of risk score models as practical tools for identifying high-risk patients and provide a quantitative assessment of the significance of various risk factors in unplanned UTI hospitalizations such as admission to ICU in the last 3 months, cognitive disorders and low inpatient, outpatient and carrier costs in the last 6 months.
Publisher: Springer International Publishing
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: IEEE
Date: 14-12-2020
Publisher: IEEE
Date: 12-2016
Publisher: IEEE
Date: 14-12-2020
Publisher: Elsevier BV
Date: 2011
Publisher: American Society of Civil Engineers (ASCE)
Date: 06-2019
Publisher: Springer Science and Business Media LLC
Date: 30-05-2022
Publisher: IEEE
Date: 12-12-2021
Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
Date: 03-2023
Abstract: This paper introduces a major redesign of SimOpt, a testbed of simulation-optimization (SO) problems and solvers. The testbed promotes the empirical evaluation and comparison of solvers and aims to accelerate their development. Relative to previous versions of SimOpt, the redesign ports the code to an object-oriented architecture in Python uses an implementation of the MRG32k3a random number generator that supports streams, substreams, and subsubstreams supports the automated use of common random numbers for ease and efficiency includes a powerful suite of plotting tools for visualizing experiment results uses bootstrapping to obtain error estimates accommodates the use of data farming to explore simulation models and optimization solvers as their input parameters vary and provides a graphical user interface. The SimOpt source code is available on a GitHub repository under a permissive open-source license and as a Python package. History: Accepted by Ted Ralphs, Area Editor for Software Tools. Funding: This work was supported by the National Science Foundation [Grant CMMI-2035086]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( oi/suppl/10.1287/ijoc.2023.1273 ) as well as from the IJOC GitHub software repository ( github.com/INFORMSJoC/2022.0011 ) at ( 0.5281/zenodo.7468744 ).
Publisher: Public Library of Science (PLoS)
Date: 27-06-2013
Publisher: IEEE
Date: 11-12-2022
Publisher: Society for Industrial & Applied Mathematics (SIAM)
Date: 2018
DOI: 10.1137/15M1042425
Location: Iran (Islamic Republic of)
Start Date: 2023
End Date: 2025
Funder: Directorate for Engineering
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