2015 Informs Annual Meeting

TD32

INFORMS Philadelphia – 2015

4 - Modeling Time-varying Autocorrelation for Time Series Classification

5 - Enterprise Social Networking and Firm Creativity Donghyun Kim, Delta State University, 1003 West Sunflower Road, Cleveland, MS, 38733, United States of America, dkim@deltastate.edu, Jaemin Kim This study examines the influence of firm’s IT social networking (SN) capacity on the firm’s creativity and innovation. Analyzing data on utility patents of 7 firms using enterprise SN, we tested our predictions on a balanced panel of the firms’ data. The results illustrate how IT SN capacity can aid in the generation of an idea. TD33 33-Room 410, Marriott Decision and Prediction Models in Healthcare Sponsor: Health Applications Sponsored Session Chair: Jakob Kotas, University of Washington, Dept. of Applied Mathematics, Box 353925, Seattle, WA, 98195, United States of America, jkotas@uw.edu 1 - A Stochastic Program with Chance-constrained Recourse for Surgery Scheduling and Rescheduling Gabriel Zenarosa, PhD Candidate, University of Pittsburgh, 3700 Aggregate surgical expenditures in the US amount to a significant percentage of GDP. About 42% of hospital revenues are generated by operating rooms (ORs), yet ORs run at only 68% capacity on average. The most important issues in OR management are centered on scheduling. Advance schedules improve OR efficiency; however, surgeries are rescheduled in practice as they rarely go as planned. We present a stochastic program with chance-constrained recourse for surgery scheduling and rescheduling. 2 - Dynamic Scheduling of a Post-discharge Follow-up Organization to Reduce Readmissions Sean Yu, Indiana University-Bloomington, 1275 E. Tenth Street, Hospital readmissions are a growing problem. Many readmissions are preventable by properly monitoring patients post-discharge. We consider an organization that dynamically will schedule and staff post-discharge monitoring schedules for a cohort of patients being randomly discharged from client hospitals. We formulate this problem as an infinite horizon dynamic program that can be solved using approximate dynamic programming. 3 - Predictive Capabilities in Hierarchical Node-based Clustering of Time Series Flu activity is shown to be affected by local variables such as climate. Therefore, localized activity must be monitored to study spatiotemporal spread patterns of the disease. Using a 10-year flu dataset from 103 hospitals in Ontario, we compare predictive capabilities extracted from existing aggregation scheme (LHIN) with those extracted from the novel hierarchical node-based clustering scheme and show that the new method will extract more statistically significant predictive capabilities. 4 - A Stochastic Dynamic Programming Model for Response-guided Dosing Jakob Kotas, University of Washington, Dept. of Applied Mathematics, Box 353925, Seattle, WA, 98195, United States of America, jkotas@uw.edu, Archis Ghate We discuss a stochastic dynamic programming (DP) model to assist with dosing decisions in response-guided dosing (RGD). The goal in this framework is to deliver the right dose to the right patient at the right time. We present robust, optimal learning, and optimal stopping variants of this problem. The structure of optimal policies in these problems will be explored both analytically and numerically. O’Hara Street, Benedum Hall 1048, Pittsburgh, PA, 15261-3048, United States of America, glz5@pitt.edu, Andrew J. Schaefer, Oleg Prokopyev Bloomington, IN, 47405, United States of America, xy9@indiana.edu, Shanshan Hu, Jonathan Helm Hootan Kamran, PhD Candidate, University of Toronto, 12 Rodney Blvd., North York, ON, M2N4B6, Canada, hootan@mie.utoronto.ca, Dionne Aleman, Kieran Moore, Mike Carter

Mustafa Gokce Baydogan, Assistant Professor, Bogaziçi University, Department of Industrial Engineering, Bebek, Istanbul, 34342, Turkey, mustafa.baydogan@boun.edu.tr, George Runger

We introduce a novel approach to model the dependency structure in time series (TS) that generalizes the concept of autoregression to local auto-patterns. A learning strategy that is fast and insensitive to parameter settings is the basis for the approach. This unsupervised approach to represent TS generally applies to a number of data mining tasks. We provide a research direction that breaks from the linear dependency models to potentially foster other promising nonlinear approaches. TD32 32-Room 409, Marriott Data Mining Contributed Session Chair: Gustavo Lujan-Moreno, Arizona State University, Tempe, AZ, United States of America, glujanmo@asu.edu 1 - Open-source Statistical Packages: The True Cost of “Free” Software Ronald Klimberg, Saint Joseph’s University, 35 Moorlinch Blvd., Medford, NJ, 08055, United States of America, klimberg@sju.edu, Rick Pollack, Susan Foltz Boklage Open source software is typically free and widely accessible to the public. In the statistical realm, R is the dominant open-source player. Is it really free? Where do you go for support? Are their possible significant costs associated with using R? Further, to what degree should open-source statistical software be used and taught in academia? This article will explore these questions, as well as others, in discussing what are often the hidden costs of using open-source statistical software 2 - Study on Effects on Emotional Intensities of Negative Online Reviews on its Usefulness Cuiping Li, Huazhong University of Science and Technology, 1037 Luoyu Rd, Hongshan District,Wuhan, Hubei, 430074, China, 412543536@qq.com, Qian Yuan, Shuqin Cai Aimed at recognizing high quality reviews from mass data, this paper explores how reviews’ negative emotions influence the usefulness of negative online reviews by using data mining technology and regression analysis. The result reveals that strong negative emotions reduce negative reviews’ usefulness and moderate negative emotions have opposite effect. Results also show that different intensities of negative emotions have significant interactions on reviews’ usefulness. 3 - Impact of Library Online Resource use on Students Academic Outcome Fan Zhang, University of Pittsburgh, 1048 Benedum Hall, Department of Industrial Engineering, Pittsburgh, PA, 15261, United States of America, faz31@pitt.edu, Louis Luangkesorn, Ziyi Kang, Yunjie Zhang, Shi Tang University libraries have a need to demonstrate the impact of their resources on the University mission: academics and research. However, for electronic resources, research has shown that students often do not recognize they are using library resources, making surveys and other assessments not useful. We use undergraduate demographic and academic outcome data along with logs of online library resource access to determine if relationship exists between online resource use and academic outcome. 4 - A Case-Crossover Study to Evaluate the Effect of Player Affective State on Performance in Video Game Gustavo Lujan-Moreno, Arizona State University, Tempe, AZ, United States of America, glujanmo@asu.edu Using an electroencephalogram (EEG) headset we examined whether there was a significant change in the affective state reported by the EEG when a participant made a mistake while playing a popular video game. There were five affective constructs that were examined: engagement, frustration, meditation, short and long term excitement. We propose a case-crossover methodology to analyze this type of events. Results show that there is a significant difference in three affective states.

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