@article{li_tobey_mayorga_caltagirone_ozaltin_2023, title={Detecting Human Trafficking: Automated Classification of Online Customer Reviews of Massage Businesses}, volume={2}, ISSN={["1526-5498"]}, DOI={10.1287/msom.2023.1196}, abstractNote={Problem definition: Approximately 11,000 alleged illicit massage businesses (IMBs) exist across the United States hidden in plain sight among legitimate businesses. These illicit businesses frequently exploit workers, many of whom are victims of human trafficking, forced or coerced to provide commercial sex. Academic/practical relevance: Although IMB review boards like Rubmaps.ch can provide first-hand information to identify IMBs, these sites are likely to be closed by law enforcement. Open websites like Yelp.com provide more accessible and detailed information about a larger set of massage businesses. Reviews from these sites can be screened for risk factors of trafficking. Methodology: We develop a natural language processing approach to detect online customer reviews that indicate a massage business is likely engaged in human trafficking. We label data sets of Yelp reviews using knowledge of known IMBs. We develop a lexicon of key words/phrases related to human trafficking and commercial sex acts. We then build two classification models based on this lexicon. We also train two classification models using embeddings from the bidirectional encoder representations from transformers (BERT) model and the Doc2Vec model. Results: We evaluate the performance of these classification models and various ensemble models. The lexicon-based models achieve high precision, whereas the embedding-based models have relatively high recall. The ensemble models provide a compromise and achieve the best performance on the out-of-sample test. Our results verify the usefulness of ensemble methods for building robust models to detect risk factors of human trafficking in reviews on open websites like Yelp. Managerial implications: The proposed models can save countless hours in IMB investigations by automatically sorting through large quantities of data to flag potential illicit activity, eliminating the need for manual screening of these reviews by law enforcement and other stakeholders. Funding: This work was supported by the National Science Foundation [Grant 1936331]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1196 .}, journal={M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT}, author={Li, Ruoting and Tobey, Margaret and Mayorga, Maria E. and Caltagirone, Sherrie and Ozaltin, Osman Y.}, year={2023}, month={Feb} }
 @article{paramita_agor_mayorga_ivy_miller_ozaltin_2023, title={Quantifying association and disparities between diabetes complications and COVID-19 outcomes: A retrospective study using electronic health records}, volume={18}, ISSN={["1932-6203"]}, DOI={10.1371/journal.pone.0286815}, abstractNote={Background Despite established relationships between diabetic status and an increased risk for COVID-19 severe outcomes, there is a limited number of studies examining the relationships between diabetes complications and COVID-19-related risks. We use the Adapted Diabetes Complications Severity Index to define seven diabetes complications. We aim to understand the risk for COVID-19 infection, hospitalization, mortality, and longer length of stay of diabetes patients with complications. Methods We perform a retrospective case-control study using Electronic Health Records (EHRs) to measure differences in the risks for COVID-19 severe outcomes amongst those with diabetes complications. Using multiple logistic regression, we calculate adjusted odds ratios (OR) for COVID-19 infection, hospitalization, and in-hospital mortality of the case group (patients with diabetes complications) compared to a control group (patients without diabetes). We also calculate adjusted mean difference in length of stay between the case and control groups using multiple linear regression. Results Adjusting demographics and comorbidities, diabetes patients with renal complications have the highest odds for COVID-19 infection (OR = 1.85, 95% CI = [1.71, 1.99]) while those with metabolic complications have the highest odds for COVID-19 hospitalization (OR = 5.58, 95% CI = [3.54, 8.77]) and in-hospital mortality (OR = 2.41, 95% CI = [1.35, 4.31] ) . The adjusted mean difference (MD) of hospital length-of-stay for diabetes patients, especially those with cardiovascular (MD = 0.94, 95% CI = [0.17, 1.71]) or peripheral vascular (MD = 1.72, 95% CI = [0.84, 2.60]) complications, is significantly higher than non-diabetes patients. African American patients have higher odds for COVID-19 infection (OR = 1.79, 95% CI = [1.66, 1.92]) and hospitalization (OR = 1.62, 95% CI = [1.39, 1.90]) than White patients in the general diabetes population. However, White diabetes patients have higher odds for COVID-19 in-hospital mortality. Hispanic patients have higher odds for COVID-19 infection (OR = 2.86, 95% CI = [2.42, 3.38]) and shorter mean length of hospital stay than non-Hispanic patients in the general diabetes population. Although there is no significant difference in the odds for COVID-19 hospitalization and in-hospital mortality between Hispanic and non-Hispanic patients in the general diabetes population, Hispanic patients have higher odds for COVID-19 hospitalization (OR = 1.83, 95% CI = [1.16, 2.89]) and in-hospital mortality (OR = 3.69, 95% CI = [1.18, 11.50]) in the diabetes population with no complications. Conclusions The presence of diabetes complications increases the risks of COVID-19 infection, hospitalization, and worse health outcomes with respect to in-hospital mortality and longer hospital length of stay. We show the presence of health disparities in COVID-19 outcomes across demographic groups in our diabetes population. One such disparity is that African American and Hispanic diabetes patients have higher odds of COVID-19 infection than White and Non-Hispanic diabetes patients, respectively. Furthermore, Hispanic patients might have less access to the hospital care compared to non-Hispanic patients when longer hospitalizations are needed due to their diabetes complications. Finally, diabetes complications, which are generally associated with worse COVID-19 outcomes, might be predominantly determining the COVID-19 severity in those infected patients resulting in less demographic differences in COVID-19 hospitalization and in-hospital mortality.}, number={9}, journal={PLOS ONE}, author={Paramita, Ni Luh Putu S. P. and Agor, Joseph K. and Mayorga, Maria E. and Ivy, Julie S. and Miller, Kristen E. and Ozaltin, Osman Y.}, year={2023}, month={Sep} }
 @article{bansal_ozaltin_uzsoy_kempf_2022, title={Coordination of manufacturing and engineering activities during product transitions}, volume={4}, ISSN={["1520-6750"]}, DOI={10.1002/nav.22056}, abstractNote={Abstract Product transitions involve the replacement of products currently being produced and distributed by a firm with new products throughout the firm's supply chain. In high technology industries effective management of product transitions is crucial to long‐term success, and involves the coordination of multiple product development units and a manufacturing unit by a product division serving a particular market. Since the different units are organizationally autonomous, and the product division does not have access to their detailed technological constraints and internal operating policies, a decentralized solution is required. We develop a price‐based coordination framework using the subadditive dual of a mixed‐integer linear program that seeks to maximize the number of units whose proposed plans are included in the final solution. The proposed approach yields superior solutions to a linear‐programming‐based branch‐and‐price approach within the same computing budget. We discuss the broader applicability of this integer column generation approach, and suggest directions for future work.}, journal={NAVAL RESEARCH LOGISTICS}, author={Bansal, Ankit and Ozaltin, Osman Y. and Uzsoy, Reha and Kempf, Karl G.}, year={2022}, month={Apr} }
 @article{tobey_li_ozaltin_mayorga_caltagirone_2022, title={Interpretable models for the automated detection of human trafficking in illicit massage businesses}, volume={8}, ISSN={["2472-5862"]}, DOI={10.1080/24725854.2022.2113187}, abstractNote={Sexually oriented establishments across the United States often pose as massage businesses and force victim workers into a hybrid of sex and labor trafficking, simultaneously harming the legitimate massage industry. Stakeholders with varied goals and approaches to dismantling the illicit massage industry all report the need for multi-source data to clearly and transparently identify the worst offenders and highlight patterns in behaviors. We utilize findings from primary stakeholder interviews with law enforcement, regulatory bodies, legitimate massage practitioners, and subject-matter experts from nonprofit organizations to identify data sources and potential indicators of illicit massage businesses (IMBs). We focus our analysis on data from open sources in Texas and Florida including customer reviews and business data from Yelp.com, the U.S. Census, and GIS files such as truck stop, highway, and military base locations. We build two interpretable prediction models, risk scores and optimal decision trees, to determine the risk that a given massage establishment is an IMB. The proposed multi-source data-based approach and interpretable models can be used by stakeholders at all levels to save time and resources, serve victim-workers, and support well informed regulatory efforts.}, journal={IISE TRANSACTIONS}, author={Tobey, Margaret and Li, Ruoting and Ozaltin, Osman Y. and Mayorga, Maria E. and Caltagirone, Sherrie}, year={2022}, month={Aug} }
 @article{khorramfar_ozaltin_kempf_uzsoy_2022, title={Managing Product Transitions: A Bilevel Programming Approach}, volume={6}, ISSN={["1526-5528"]}, url={https://doi.org/10.1287/ijoc.2022.1210}, DOI={10.1287/ijoc.2022.1210}, abstractNote={We model the hierarchical and decentralized nature of product transitions using a mixed-integer bilevel program with two followers, a manufacturing unit and an engineering unit. The leader, corporate management, seeks to maximize revenue over a finite planning horizon. The manufacturing unit uses factory capacity to satisfy the demand for current products. The demand for new products, however, cannot be fulfilled until the engineering unit completes their development, which, in turn, requires factory capacity for prototype fabrication. We model this interdependency between the engineering and manufacturing units as a generalized Nash equilibrium game at the lower level of the proposed bilevel model. We present a reformulation where the interdependency between the followers is resolved through the leader’s coordination, and we derive a solution method based on constraint and column generation. Our computational experiments show that the proposed approach can solve realistic instances to optimality in a reasonable time. We provide managerial insights into how the allocation of decision authority between corporate leadership and functional units affects the objective function performance. This paper presents the first exact solution algorithm to mixed-integer bilevel programs with interdependent followers, providing a flexible framework to study decentralized, hierarchical decision-making problems.}, journal={INFORMS JOURNAL ON COMPUTING}, author={Khorramfar, Rahman and Ozaltin, Osman Y. and Kempf, Karl G. and Uzsoy, Reha}, year={2022}, month={Jun}, pages={1–17} }
 @article{agor_li_ozaltin_2022, title={Septic shock prediction and knowledge discovery through temporal pattern mining}, volume={132}, ISSN={["1873-2860"]}, DOI={10.1016/j.artmed.2022.102406}, abstractNote={Sepsis is the body's adverse response to infection which can lead to septic shock and eventually death if not treated in a timely manner. Analyzing patterns in sepsis patients' health status over time can help predict septic shock before its onset allowing healthcare providers to be more proactive. Temporal pattern mining methods can be used to identify trends in a patient's health status over time. If these methods return too many patterns, however, this can hinder knowledge discovery and practical implementation at the bedside in acute care settings. We propose a framework to find a small number of relevant temporal patterns in electronic health records for the early prediction of septic shock. Our framework consists of a temporal pattern mining method and three pattern selection techniques based on non-contrasted group support (PST1), contrasted group support (PST2), and model predictive power (PST3, PST4). We find that model-based feature selection approaches PST3 and PST4 yield the best prediction performance among these techniques. However, PST2 identifies more multi-state patterns with abnormal health states, which can give healthcare providers indicators of patient deterioration towards septic shock. Hence, from a knowledge discovery perspective, it may be worthwhile to sacrifice a small amount of prediction power for actionable patient health information through the implementation of PST2. • A framework to identify relevant temporal patterns in data is proposed. • Recent temporal pattern mining is combined with three feature selection techniques. • Septic shock prediction models are trained using temporal patterns. • There is a tradeoff between prediction performance and knowledge discovery. • Contrasted grouping provides informative patterns of patient deterioration.}, journal={ARTIFICIAL INTELLIGENCE IN MEDICINE}, author={Agor, Joseph K. and Li, Ruoting and Ozaltin, Osman Y.}, year={2022}, month={Oct} }
 @article{dalgic_wu_erenay_sir_ozaltin_crum_pasupathy_2021, title={Mapping of critical events in disease progression through binary classification: Application to amyotrophic lateral sclerosis}, volume={123}, ISSN={["1532-0480"]}, DOI={10.1016/j.jbi.2021.103895}, abstractNote={The progression of many degenerative diseases is tracked periodically using scales evaluating functionality in daily activities. Although estimating the timing of critical events (i.e., disease tollgates) during degenerative disease progression is desirable, the necessary data may not be readily available in scale records. Further, analysis of disease progression poses data challenges, such as censoring and misclassification errors, which need to be addressed to provide meaningful research findings and inform patients.We developed a novel binary classification approach to map scale scores into disease tollgates to describe disease progression leveraging standard/modified Kaplan-Meier analyses. The approach is demonstrated by estimating progression pathways in amyotrophic lateral sclerosis (ALS). Tollgate-based ALS Staging System (TASS) specifies the critical events (i.e., tollgates) in ALS progression. We first developed a binary classification predicting whether each TASS tollgate was passed given the itemized ALSFRS-R scores using 514 ALS patients' data from Mayo Clinic-Rochester. Then, we utilized the binary classification to translate/map the ALSFRS-R data of 3,264 patients from the PRO-ACT database into TASS. We derived the time trajectories of ALS progression through tollgates from the augmented PRO-ACT data using Kaplan-Meier analyses. The effects of misclassification errors, condition-dependent dropouts, and censored data in trajectory estimations were evaluated with Interval Censored Kaplan Meier Analysis and Multistate Model for Panel Data.The approach using Mayo Clinic data accurately estimated tollgate-passed states of patients given their itemized ALSFRS-R scores (AUCs > 0.90). The tollgate time trajectories derived from the augmented PRO-ACT dataset provide valuable insights; we predicted that the majority of the ALS patients would have modified arm function (67%) and require assistive devices for walking (53%) by the second year after ALS onset. By the third year, most (74%) ALS patients would occasionally use a wheelchair, while 48% of the ALS patients would be wheelchair-dependent by the fourth year. Assistive speech devices and feeding tubes were needed in 49% and 30% of the patients by the third year after ALS onset, respectively. The onset body region alters some tollgate passage time estimations by 1-2 years.The estimated tollgate-based time trajectories inform patients and clinicians about prospective assistive device needs and life changes. More research is needed to personalize these estimations according to prognostic factors. Further, the approach can be leveraged in the progression of other diseases.}, journal={JOURNAL OF BIOMEDICAL INFORMATICS}, author={Dalgic, O. Ozden and Wu, Haoran and Erenay, F. Safa and Sir, Y. Mustafa and Ozaltin, Y. Osman and Crum, A. Brian and Pasupathy, S. Kalyan}, year={2021}, month={Nov} }
 @article{agor_paramita_ozaltn_2021, title={Prediction of Sepsis Related Mortality: An Optimization Approach}, volume={25}, ISSN={["2168-2208"]}, url={https://doi.org/10.1109/JBHI.2021.3096470}, DOI={10.1109/JBHI.2021.3096470}, abstractNote={Sepsis is a condition that progresses quickly and is a major cause of mortality in hospitalized patients. Data-driven diagnostic and therapeutic interventions are essential to ensure early diagnosis and appropriate care. The Sequential Organ Failure Assessment (SOFA) score is widely utilized in clinical practice to assess septic patients for organ dysfunction. The SOFA score uses points between 0 and 4 to quantify the level of dysfunction in six organ systems. These points are determined based on expert opinion and not informed by data, thus their usefulness can vary among different medical institutions depending on the targeted use. In this study, we propose multiple strategies to adjust the SOFA score using mixed-integer programming to improve the in-hospital mortality prediction of septic patients based on Electronic Health Records (EHRs). We use the same variables and threshold values of the original SOFA score in each strategy. Thus, the proposed approach takes advantage of optimization and data analysis while taking into account the medical expertise. Our results demonstrate a statistically significant improvement ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$p< 0.001$</tex-math></inline-formula> ) in the prediction of in-hospital mortality among patients susceptible to sepsis when implementing our proposed strategies. Area under the receiver operator curve (AUC) and accuracy values of 0.8928 and 0.8904 are achieved by optimizing the point values of the SOFA score.}, number={11}, journal={IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Agor, Joseph K. and Paramita, Ni Luh Putu S. P. and Ozaltn, Osman Y.}, year={2021}, month={Nov}, pages={4207–4216} }
 @article{enayati_ozaltin_2020, title={Optimal influenza vaccine distribution with equity}, volume={283}, ISSN={["1872-6860"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85076546246&partnerID=MN8TOARS}, DOI={10.1016/j.ejor.2019.11.025}, abstractNote={This paper is concerned with the optimal influenza vaccine distribution in a heterogeneous population consisting of multiple subgroups. We employ a compartmental model for influenza transmission and formulate a mathematical program to minimize the number of vaccine doses distributed to effectively extinguish an emerging outbreak in its early stages. We propose an equity constraint to help public health authorities consider fairness when making vaccine distribution decisions. We develop an exact solution approach that generates a vaccine distribution policy with a solution quality guarantee. We perform sensitivity analyses on key epidemic parameters in order to illustrate the application of the proposed model. We then analyze the scalability of the solution approach for a population consisting of subgroups based on geographic location and age. We finally demonstrate the proposed model’s ability to consider vaccine coverage inequity and discuss a derivative-free optimization approach, as an alternative solution method which can consider various different objective functions and constraints. Our results indicate that consideration of group-specific transmission dynamics is paramount to the optimal distribution of influenza vaccines.}, number={2}, journal={EUROPEAN JOURNAL OF OPERATIONAL RESEARCH}, author={Enayati, Shakiba and Ozaltin, Osman Y.}, year={2020}, month={Jun}, pages={714–725} }
 @article{velasquez_mayorga_ozaltin_2020, title={Prepositioning disaster relief supplies using robust optimization}, volume={52}, ISSN={["2472-5862"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85081951522&partnerID=MN8TOARS}, DOI={10.1080/24725854.2020.1725692}, abstractNote={Emergency disaster managers are concerned with responding to disasters in a timely and efficient manner. We are concerned with determining the location and amount of disaster relief supplies to be prepositioned in anticipation of disasters. These supplies are stocked when the locations of affected areas and the amount of relief items needed are uncertain. Furthermore, a proportion of the prepositioned supplies might be damaged by the disasters. We propose a two-stage robust optimization model. The location and amount of prepositioned relief supplies are decided in the first stage before any disaster occurs. In the second stage, a limited amount of relief supplies can be procured post-disaster and prepositioned supplies are distributed to affected areas. The objective is to minimize the total cost of prepositioning and distributing disaster relief supplies. We solve the proposed robust optimization model using a column-and-constraint generation algorithm. Two optimization criteria are considered: absolute cost and maximum regret. A case study of the hurricane season in the Southeast US is used to gain insights on the effects of optimization criteria and critical model parameters to relief supply prepositioning strategy.}, number={10}, journal={IISE TRANSACTIONS}, author={Velasquez, German A. and Mayorga, Maria E. and Ozaltin, Osman Y.}, year={2020}, month={Oct}, pages={1122–1140} }
 @inbook{sand?k??_özalt?n_2019, title={An embarrassingly parallel method for large-scale stochastic programs}, volume={149}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85072800112&partnerID=MN8TOARS}, DOI={10.1007/978-3-030-22788-3_5}, abstractNote={Stochastic programming offers a flexible modeling framework for optimal decision-making problems under uncertainty. Most practical stochastic programming instances, however, quickly grow too large to solve on a single computer, especially due to memory limitations. This chapter reviews recent developments in solving large-scale stochastic programs, possibly with multiple stages and mixed-integer decision variables, and focuses on a scenario decomposition-based bounding method, which is broadly applicable as it does not rely on special problem structure and stands out as a natural candidate for implementation in a distributed fashion. In addition to discussing the method theoretically, this chapter examines issues related to a distributed implementation of the method on a modern computing grid. Using large-scale instances from the literature, this chapter demonstrates the potential of the method in obtaining high quality solutions to very large-scale stochastic programming instances within a reasonable time frame.}, booktitle={Springer Optimization and Its Applications}, author={Sand?k??, B. and Özalt?n, O.Y.}, year={2019}, pages={127–151} }
 @inproceedings{swan_ozaltin_hilburn_gignac_mccammon_2019, title={Evaluating an Emergency Department Care Redesign: A Simulation Approach}, volume={2019-December}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85081120564&partnerID=MN8TOARS}, DOI={10.1109/WSC40007.2019.9004947}, abstractNote={Complex interactions between workload variability, uncertain and increasing arrival rates, and resource constraints make it difficult to improve flow through emergency departments (EDs). This complexity causes crowded EDs, long patient lengths of stay, and burnout among care providers. One way to improve efficiency while maintaining high quality care is to switch from a siloed unit-based department to a team-based design or pod system. This paper seeks to compare a pod system against the unit-based design at Southeastern Health's ED using a discrete event simulation. Robustness of the model under a selection of staffing designs will be tested with increased arrival rates and varying mixes of severity for incoming patients. Ultimately, it is shown the pod system maintains quality of care metrics while increasing resource utilization, establishing proof of concept that an optimized pod system can improve flow in the ED.}, booktitle={Proceedings - Winter Simulation Conference}, author={Swan, B. and Ozaltin, O. and Hilburn, S. and Gignac, E. and McCammon, G.}, year={2019}, pages={1137–1147} }
 @article{agor_ozaltin_2019, title={Feature selection for classification models via bilevel optimization}, volume={106}, ISSN={["1873-765X"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85047058360&partnerID=MN8TOARS}, DOI={10.1016/j.cor.2018.05.005}, abstractNote={Selecting model features that would ensure adequate out-of-sample classification is difficult in real life applications of classification often because there is a large number of candidate features. We propose a bilevel programming approach to feature selection problem for classification and develop a novel genetic algorithm as a solution approach. We implement the proposed framework in three different case studies where we classify influenza strains based on antigenic variety, distinguish between good and bad quality colposcopy images, and identify splice junction sites in genetic sequences. As a benchmark for the proposed genetic algorithm, we use a derivative-free optimization method to solve the bilevel feature selection problems in these case studies. The computational experiments show that the proposed bilevel framework improves the overall classification performance while selecting the most important features for the model.}, journal={COMPUTERS & OPERATIONS RESEARCH}, author={Agor, Joseph and Ozaltin, Osman Y.}, year={2019}, month={Jun}, pages={156–168} }
 @inproceedings{abu-el-haija_ivy_ozaltin_park_2019, title={The Effect of the Distribution of the Inverse Growth Rate on Pancreatic Cancer Progression}, volume={2019-December}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85081113882&partnerID=MN8TOARS}, DOI={10.1109/WSC40007.2019.9004877}, abstractNote={Pancreatic cancer is a low-incidence disease, where tumor progression studies using patient longitudinal data had limited sample sizes. Estimating the tumor inverse growth rate and its distribution are a challenge. Using a tumor progression model that incorporates the distribution of the inverse growth rate as the underlying assumption of the model, pancreatic cancer progression models were built assuming two distributions for the inverse growth rate: Uniform and Gamma. This study uses simulation to evaluate the effect of the tumor inverse growth rate distribution on the tumor progression models by examining tumor timelines. It was found that the tumor timeline is about nine months longer under the assumption that the inverse growth rate follows Gamma distribution. It was inconclusive whether tumor progression is faster or slower in older patients as the tumor progression models with the different underlying assumptions on the inverse growth rate yielded opposite results.}, booktitle={Proceedings - Winter Simulation Conference}, author={Abu-El-Haija, L. and Ivy, J.S. and Ozaltin, O. and Park, W.}, year={2019}, pages={1044–1054} }
 @article{agor_ozaltin_ivy_capan_arnold_romero_2019, title={The value of missing information in severity of illness score development}, volume={97}, ISSN={["1532-0480"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85069932839&partnerID=MN8TOARS}, DOI={10.1016/j.jbi.2019.103255}, abstractNote={We aim to investigate the hypothesis that using information about which variables are missing along with appropriate imputation improves the performance of severity of illness scoring systems used to predict critical patient outcomes.We quantify the impact of missing and imputed variables on the performance of prediction models used in the development of a sepsis-related severity of illness scoring system. Electronic health records (EHR) data were compiled from Christiana Care Health System (CCHS) on 119,968 adult patients hospitalized between July 2013 and December 2015. Two outcomes of interest were considered for prediction: (1) first transfer to intensive care unit (ICU) and (2) in-hospital mortality. Five different prediction models were employed. Indicators were utilized in these prediction models to identify when variables were missing and imputed.We observed statistically significant gains in prediction performance when moving from models that did not indicate missing information to those that did. Moreover, this increase was higher in models that use summary variables as predictors compared to those that use all variables.When developing prediction models using longitudinal EHR data, researchers should explore the incorporation of indicators for missing variables along with appropriate imputation.}, journal={JOURNAL OF BIOMEDICAL INFORMATICS}, author={Agor, Joseph and Ozaltin, Osman Y. and Ivy, Julie S. and Capan, Muge and Arnold, Ryan and Romero, Santiago}, year={2019}, month={Sep} }
 @article{dalgic_erenay_pasupathy_ozaltin_crum_sir_2019, title={Tollgate-based progression pathways of ALS patients}, volume={266}, ISSN={["1432-1459"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85060675048&partnerID=MN8TOARS}, DOI={10.1007/s00415-019-09199-y}, number={3}, journal={JOURNAL OF NEUROLOGY}, author={Dalgic, Ozden O. and Erenay, F. Safa and Pasupathy, Kalyan S. and Ozaltin, Osman Y. and Crum, Brian A. and Sir, Mustafa Y.}, year={2019}, month={Mar}, pages={755–765} }
 @article{enayati_ozaltin_mayorga_saydam_2018, title={Ambulance redeployment and dispatching under uncertainty with personnel workload limitations}, volume={50}, ISSN={["2472-5862"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85048249953&partnerID=MN8TOARS}, DOI={10.1080/24725854.2018.1446105}, abstractNote={Emergency Medical Services (EMS) managers are concerned with responding to emergency calls in a timely manner. Redeployment and dispatching strategies can be used to improve coverage that pertains to the proportion of calls that are responded to within a target time threshold. Dispatching refers to the choice of which ambulance to send to a call, and redeployment refers to repositioning of idle ambulances to compensate for coverage loss due to busy ambulances. Redeployment moves, however, impose additional workload on EMS personnel and must be executed with care. We propose a two-stage stochastic programming model to redeploy and dispatch ambulances to maximize the expected coverage. Our model restricts personnel workload in a shift and incorporates multiple call priority levels. We develop a Lagrangian branch-and-bound algorithm to solve realistic size instances. We evaluate the model performance based on average coverage and average ambulance workload during a shift. Our computational results indicate that the proposed Lagrangian branch-and-bound is significantly more efficient than CPLEX, especially for large problem instances. We also compare our model with benchmarks from the literature and show that it can improve the performance of an EMS system considerably, in particular with respect to mean response time to high-priority calls.}, number={9}, journal={IISE TRANSACTIONS}, author={Enayati, Shakiba and Ozaltin, Osman Y. and Mayorga, Maria E. and Saydam, Cem}, year={2018}, pages={777–788} }
 @misc{agor_ozaltin_2018, title={Models for predicting the evolution of influenza to inform vaccine strain selection}, volume={14}, ISSN={["2164-554X"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85044212421&partnerID=MN8TOARS}, DOI={10.1080/21645515.2017.1423152}, abstractNote={Influenza vaccine composition is reviewed before every flu season because influenza viruses constantly evolve through antigenic changes. To inform vaccine updates, laboratories that contribute to the World Health Organization Global Influenza Surveillance and Response System monitor the antigenic phenotypes of circulating viruses all year round. Vaccine strains are selected in anticipation of the upcoming influenza season to allow adequate time for production. A mismatch between vaccine strains and predominant strains in the flu season can significantly reduce vaccine effectiveness. Models for predicting the evolution of influenza based on the relationship of genetic mutations and antigenic characteristics of circulating viruses may inform vaccine strain selection decisions. We review the literature on state-of-the-art tools and prediction methodologies utilized in modeling the evolution of influenza to inform vaccine strain selection. We then discuss areas that are open for improvement and need further research.}, number={3}, journal={HUMAN VACCINES & IMMUNOTHERAPEUTICS}, author={Agor, Joseph K. and Ozaltin, Osman Y.}, year={2018}, pages={678–683} }
 @article{zare_özalt?n_prokopyev_2018, title={On a class of bilevel linear mixed-integer programs in adversarial settings}, volume={71}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85027984872&partnerID=MN8TOARS}, DOI={10.1007/s10898-017-0549-2}, number={1}, journal={Journal of Global Optimization}, author={Zare, M.H. and Özalt?n, O.Y. and Prokopyev, O.A.}, year={2018}, pages={91–113} }
 @article{özaltin_prokopyev_schaefer_2018, title={Optimal design of the seasonal influenza vaccine with manufacturing autonomy}, volume={30}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85047815039&partnerID=MN8TOARS}, DOI={10.1287/ijoc.2017.0786}, abstractNote={AboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to Section HomeINFORMS Journal on ComputingVol. 30, No. 2 Optimal Design of the Seasonal Influenza Vaccine with Manufacturing AutonomyOsman Y. Özaltın , Oleg A. Prokopyev , Andrew J. Schaefer Osman Y. Özaltın , Oleg A. Prokopyev , Andrew J. Schaefer Published Online:25 May 2018https://doi.org/10.1287/ijoc.2017.0786AbstractInfluenza (flu) is a serious public health concern. The first line of defense is the flu shot, whose composition is updated annually to adjust for frequent mutations of the circulating viruses. The World Health Organization recommends which strains to include in the flu shot based on global surveillance. Vaccine manufacturers produce trivalent and quadrivalent flu shots. The design of the flu shot, however, affects the manufacturers’ capacity and profit. In return, production decisions of the manufacturers affect the societal vaccination benefit by determining coverage and timely availability. We model this two-level hierarchy using a bilevel multistage stochastic mixed-integer program. Calibrated with publicly available data, our model integrates the flu shot composition and manufacturing in a stochastic and dynamic environment. We derive a branch-and-price algorithm to find the global optimal solution. We also propose an effective heuristic to provide the public health planners with a decision aid tool. Finally, we perform numerical experiments to answer important public health policy questions and to quantify the impact of the proposed modeling extensions. A major conclusion of our work is that the vaccine strain of a category that is not expected to be very prevalent and/or that is unlikely to drift in the upcoming season should be selected as early as possible, especially when the selections for other strain categories have to be postponed to improve the flu shot design.The online appendix is available at https://doi.org/10.1287/ijoc.2017.0786. Previous Back to Top Next FiguresReferencesRelatedInformationCited byManaging Product Transitions: A Bilevel Programming ApproachRahman Khorramfar, Osman Y. ÖzaltınKarl G. Kempf, Reha Uzsoy30 June 2022 | INFORMS Journal on Computing, Vol. 34, No. 5 Volume 30, Issue 2Spring 2018Pages 217-420 Article Information Supplemental Materials Metrics Information Received:January 22, 2015Accepted:August 31, 2017Published Online:May 25, 2018 Copyright © 2018, INFORMSCite asOsman Y. Özaltın, Oleg A. Prokopyev, Andrew J. Schaefer (2018) Optimal Design of the Seasonal Influenza Vaccine with Manufacturing Autonomy. INFORMS Journal on Computing 30(2):371-387. https://doi.org/10.1287/ijoc.2017.0786 Keywordsinfluenza vaccinemultistage stochastic mixed-integer programmingbilevel programminginteger programmingPDF download}, number={2}, journal={INFORMS Journal on Computing}, author={Özaltin, O.Y. and Prokopyev, O.A. and Schaefer, A.J.}, year={2018}, pages={371–387} }
 @article{jansen_ozaltin_2018, title={Optimal production in a competitive market under yield uncertainty}, volume={12}, ISSN={["1862-4480"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85049004706&partnerID=MN8TOARS}, DOI={10.1007/s11590-018-1288-0}, number={7}, journal={OPTIMIZATION LETTERS}, author={Jansen, Maria C. and Ozaltin, Osman Y.}, year={2018}, month={Oct}, pages={1487–1502} }
 @article{sandikci_ozaltin_2017, title={A SCALABLE BOUNDING METHOD FOR MULTISTAGE STOCHASTIC PROGRAMS}, volume={27}, ISSN={["1095-7189"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85028700659&partnerID=MN8TOARS}, DOI={10.1137/16m1075594}, abstractNote={Many dynamic decision problems involving uncertainty can be appropriately modeled as multistage stochastic programs. However, most practical instances are so large and/or complex that it is impossible to solve them on a single computer, especially due to memory limitations. Extending the work of [B. Sandikci, N. Kong, and A. J. Schaefer, Math. Program., 138 (2013), pp. 253--272] on two-stage stochastic mixed-integer programs, this paper considers general multistage stochastic programs and develops a bounding method based on scenario decomposition. This method is broadly applicable, as it does not assume any problem structure including convexity. Moreover, it naturally fits into a distributed computing environment. Computational experiments with large-scale instances (with up to 100 million scenarios, about 1.5 billion decision variables---85% binary---and 800 million constraints) demonstrate that the proposed method scales nicely with problem size and has immense potential to obtain high-quality solutions...}, number={3}, journal={SIAM JOURNAL ON OPTIMIZATION}, author={Sandikci, Buhaneddin and Ozaltin, Osman Y.}, year={2017}, pages={1772–1800} }
 @article{dalgic_ozaltin_ciccotelli_erenay_2017, title={Deriving effective vaccine allocation strategies for pandemic influenza: Comparison of an agent-based simulation and a compartmental model}, volume={12}, ISSN={["1932-6203"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85013497979&partnerID=MN8TOARS}, DOI={10.1371/journal.pone.0172261}, abstractNote={Individuals are prioritized based on their risk profiles when allocating limited vaccine stocks during an influenza pandemic. Computationally expensive but realistic agent-based simulations and fast but stylized compartmental models are typically used to derive effective vaccine allocation strategies. A detailed comparison of these two approaches, however, is often omitted. We derive age-specific vaccine allocation strategies to mitigate a pandemic influenza outbreak in Seattle by applying derivative-free optimization to an agent-based simulation and also to a compartmental model. We compare the strategies derived by these two approaches under various infection aggressiveness and vaccine coverage scenarios. We observe that both approaches primarily vaccinate school children, however they may allocate the remaining vaccines in different ways. The vaccine allocation strategies derived by using the agent-based simulation are associated with up to 70% decrease in total cost and 34% reduction in the number of infections compared to the strategies derived by using the compartmental model. Nevertheless, the latter approach may still be competitive for very low and/or very high infection aggressiveness. Our results provide insights about potential differences between the vaccine allocation strategies derived by using agent-based simulations and those derived by using compartmental models.}, number={2}, journal={PLOS ONE}, author={Dalgic, Ozden O. and Ozaltin, Osman Y. and Ciccotelli, William A. and Erenay, Fatih S.}, year={2017}, month={Feb} }
 @article{jansen_ozaltin_2017, title={Note on Cournot Competition Under Yield Uncertainty}, volume={19}, ISSN={["1526-5498"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85019130358&partnerID=MN8TOARS}, DOI={10.1287/msom.2016.0610}, abstractNote={Inspired by the U.S. influenza vaccine market, we formulate a Cournot competition model with asymmetric firms facing capacity constraints and yield uncertainty. We derive the equilibrium of this model by defining a score that ranks firms based on their capacity, unit production cost, random yield mean, and variance. In particular, we show a threshold structure. Firms that have scores above a threshold produce at full capacity, while other firms produce less than their capacities in the equilibrium. Finally, for the case of symmetric firms, we correct mistakes in the analysis of Deo and Corbett [Deo S, Corbett CJ (2009) Cournot competition under yield uncertainty: The case of the U.S. influenza vaccine market. Manufacturing Service Oper. Management 11(4):563–576], showing that society can benefit from supplier diversification under weaker conditions. The online appendices are available at https://doi.org/10.1287/msom.2016.0610 .}, number={2}, journal={M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT}, author={Jansen, Maria C. and Ozaltin, Osman Y.}, year={2017}, pages={305–308} }
 @inproceedings{cleghern_lahiri_ozaltin_roberts_2017, title={Predicting future states in DotA 2 using value-split models of time series attribute data}, volume={Part F130151}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85030792157&partnerID=MN8TOARS}, DOI={10.1145/3102071.3102095}, abstractNote={In Multiplayer Online Battle Arena (MOBA) games, teams of players compete in combat to complete an objective and defeat the opposing team. To stay alive, players must closely monitor their character's status, especially remaining health. Understanding how health may change in the near future can be vital in determining what tactics a player may use. We analyzed replay logs of the game Defense of the Ancients 2 (DotA 2) to discover methods to predict how players' health evolves over time. For DotA 2, our results suggest that forecasting changes in a player's health can be done by viewing gameplay as two separate processes: normal gameplay flow in which changes in health are smaller and more regular, and less frequent but higher-impact events in which players experience larger changes in their health, such as team battles. We accomplished this by considering health data as two separate, but interleaved, time series in which separate processes govern low magnitude changes in health from high magnitude changes. In this paper, we present a value-split approach to predicting changes in health and describe the results of our approach using autoregressive moving-average models for low magnitude health changes and a combination of statistical models for the larger changes.}, booktitle={ACM International Conference Proceeding Series}, author={Cleghern, Z. and Lahiri, S. and Ozaltin, Osman and Roberts, D.L.}, year={2017} }
 @inproceedings{agor_mckenzie_mayorga_ozaltin_parikh_huddleston_2017, title={Simulating triage of patients into an internal medicine department to validate the use of an optimization-based workload score}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85044522866&partnerID=MN8TOARS}, DOI={10.1109/wsc.2017.8248011}, booktitle={2017 winter simulation conference (wsc)}, author={Agor, J. and McKenzie, K. and Mayorga, M. E. and Ozaltin, Osman and Parikh, R. S. and Huddleston, J.}, year={2017}, pages={2881–2892} }
 @article{zhang_ozaltin_2017, title={Single-ratio fractional integer programs with stochastic right-hand sides}, volume={49}, ISSN={["2472-5862"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85019852798&partnerID=MN8TOARS}, DOI={10.1080/24725854.2017.1302116}, abstractNote={ABSTRACTWe present an equivalent value function reformulation for a class of single-ratio Fractional Integer Programs (FIPs) with stochastic right-hand sides and propose a two-phase solution approach. The first phase constructs the value functions of FIPs in both stages. The second phase solves the reformulation using a global branch-and-bound algorithm or a level-set approach. We derive some basic properties of the value functions of FIPs and utilize them in our algorithms. We show that in certain cases our approach can solve instances whose extensive forms have the same order of magnitude as the largest stochastic quadratic integer programs solved in the literature.}, number={6}, journal={IISE TRANSACTIONS}, author={Zhang, Junlong and Ozaltin, Osman Y.}, year={2017}, pages={579–592} }
 @inproceedings{ozaltin_dalgic_erenay_2016, title={Optimal distribution of the influenza vaccine}, volume={2015-January}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84940470291&partnerID=MN8TOARS}, DOI={10.1109/wsc.2014.7019995}, abstractNote={Influenza is a serious public health concern and vaccination is the first line of defense. In a pandemic, individuals are prioritized based on their risk profiles and transmission rates to ensure effective use of the available vaccine. We use an agent-based stochastic simulation model, and optimize the age-specific vaccine distribution strategy. We use black-box optimization techniques to minimize the overall cost of the outbreak. Our numerical experiments show that the best policy returned by our approach outperforms alternative policies recommended by the Advisory Committee on Immunization Practices and Centers for Disease Control and Prevention.}, booktitle={2016 10th european conference on antennas and propagation (eucap)}, author={Ozaltin, Osman and Dalgic, O. O. and Erenay, F. S.}, year={2016}, pages={1411–1420} }
 @inproceedings{agor_mckenzie_ozaltin_mayorga_parikh_huddleston_2016, title={Simulation of triaging patients into an internal medicine department to validate the use of an optimization based workload score}, volume={0}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85014275794&partnerID=MN8TOARS}, DOI={10.1109/wsc.2016.7822411}, booktitle={2016 winter simulation conference (wsc)}, author={Agor, J. and McKenzie, K. and Ozaltin, Osman and Mayorga, M. and Parikh, R. S. and Huddleston, J.}, year={2016}, pages={3708–3709} }
 @article{beheshti_ozaltin_zare_prokopyev_2015, title={Exact solution approach for a class of nonlinear bilevel knapsack problems}, volume={61}, ISSN={["1573-2916"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84922834588&partnerID=MN8TOARS}, DOI={10.1007/s10898-014-0189-8}, number={2}, journal={JOURNAL OF GLOBAL OPTIMIZATION}, author={Beheshti, Behdad and Ozaltin, Osman Y. and Zare, M. Hosein and Prokopyev, Oleg A.}, year={2015}, month={Feb}, pages={291–310} }
 @article{özaltin_prokopyev_schaefer_2012, title={Two-stage quadratic integer programs with stochastic right-hand sides}, volume={133}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84862285520&partnerID=MN8TOARS}, DOI={10.1007/s10107-010-0412-4}, number={1-2}, journal={Mathematical Programming}, author={ÖzaltIn, O.Y. and Prokopyev, O.A. and Schaefer, A.J.}, year={2012}, pages={121–158} }
 @article{oezaltin_prokopyev_schaefer_roberts_2011, title={Optimizing the Societal Benefits of the Annual Influenza Vaccine: A Stochastic Programming Approach}, volume={59}, ISSN={["0030-364X"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-81455159292&partnerID=MN8TOARS}, DOI={10.1287/opre.1110.0988}, abstractNote={Seasonal influenza is a major public health concern, and the first line of defense is the flu shot. Antigenic drifts and the high rate of influenza transmission require annual updates to the flu shot composition. The World Health Organization recommends which flu strains to include in the annual vaccine, based on surveillance and epidemiological analysis. There are two critical decisions regarding the flu shot design. One is its composition; currently, three strains constitute the flu shot, and they influence vaccine effectiveness. Another critical decision is the timing of the composition decisions, which affects the flu shot production. Both of these decisions have to be made under uncertainty many months before the flu season starts. We quantify the trade-offs involved through a multistage stochastic mixed-integer program that determines the optimal flu shot composition and its timing in a stochastic and dynamic environment.We incorporate risk sensitivity through mean-risk models. Our results provide valuable insights for pressing policy issues.}, number={5}, journal={OPERATIONS RESEARCH}, author={Oezaltin, Osman Y. and Prokopyev, Oleg A. and Schaefer, Andrew J. and Roberts, Mark S.}, year={2011}, pages={1131–1143} }
 @article{özaltin_hunsaker_schaefer_2011, title={Predicting the solution time of branch-and-bound algorithms for mixed-integer programs}, volume={23}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-79961066970&partnerID=MN8TOARS}, DOI={10.1287/ijoc.1100.0405}, abstractNote={The most widely used progress measure for branch-and-bound (B&B) algorithms when solving mixed-integer programs (MIPs) is the MIP gap. We introduce a new progress measure that is often much smoother than the MIP gap. We propose a double exponential smoothing technique to predict the solution time of B&B algorithms and evaluate the prediction method using three MIP solvers. Our computational experiments show that accurate predictions of the solution time are possible, even in the early stages of B&B algorithms.}, number={3}, journal={INFORMS Journal on Computing}, author={Özaltin, O.Y. and Hunsaker, B. and Schaefer, A.J.}, year={2011}, pages={392–403} }
 @article{özaltin_prokopyev_schaefer_2010, title={The bilevel knapsack problem with stochastic right-hand sides}, volume={38}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-77955306156&partnerID=MN8TOARS}, DOI={10.1016/j.orl.2010.04.005}, abstractNote={We introduce the bilevel knapsack problem with stochastic right-hand sides, and provide necessary and sufficient conditions for the existence of an optimal solution. When the leader’s decisions can take only integer values, we present an equivalent two-stage stochastic programming reformulation with binary recourse. We develop a branch-and-cut algorithm for solving this reformulation, and a branch-and-backtrack algorithm for solving the scenario subproblems. Computational experiments indicate that our approach can solve large instances in a reasonable amount of time.}, number={4}, journal={Operations Research Letters}, author={Özaltin, O.Y. and Prokopyev, O.A. and Schaefer, A.J.}, year={2010}, pages={328–333} }