2016 INFORMS Annual Meeting Program

WA03

WC42INFORMS Charlotte – 2011

Wednesday, 8:00AM - 9:30AM

2 - Understanding The Association Of Clinical Characteristics Of Low Grade Gliomas With Disease Outcomes Anh Pham, Student, University of Arkansas, 1 University Avenue, Fayetteville, AR, 72701, United States, anh.pham1234@gmail.com, Shengfan Zhang Glioma is among the most prevalent and most devastating primary brain tumor. Gliomas represent 28% of all brain tumors and 80% of malignant brain tumors. 70% of Low Grade Glioma patients eventually die from cancerous tumor transformation. This study uses The Cancer Genome Atlas (TCGA) data to understand relationships between different clinical characteristics of Low Grade Glioma, such as tumor grades, tumor status, vital status and first presented symptoms. Two data mining methods, association rules and decision trees, are used. 3 - Modeling The Accumulation Of Comorbidities In Patients With Multiple Chronic Conditions Adel Alaeddini, University of Texas at San Antonio, Department of Mechanical Engineering, One UTSA Circle, San Antonio, TX, 78249, United States, adel.alaeddini@utsa.edu Long-lasting diseases known as chronic conditions can be considered as a staple example of degradation processes that can progress and accumulate over time. Approximately a quarter of all Americans and 75% of citizens aged 65 years and older are burdened with two or more (multiple) chronic conditions (MCC). Here, we introduce a latent regression Markov mixture (LRMM) model to explore major patterns of disease accumulation in MCC patients and identify the risk factors affecting the accumulation process. The new methodology will be validated through a national healthcare dataset. WA03 101C-MCC Big Data Contributed Session 1 - Discriminant Analysis And The Baseball Hall Of Fame Tom Brady, Purdue University, 1401 S US Hwy 421, Westville, IN, 46391, United States, tbradyjr@pnc.edu, Tom Brady Baseball has long been referred to as the national past time in America. The most fundamental discussions center around the inclusion, or exclusion of individual players in the Baseball Hall of Fame. Election to this esteemed organization is done on a purely subjective basis. The movie “MoneyBall” has highlighted the recent trend towards using a more quantitative approach to managing and operating a professional baseball team. The term Sabermetrics refers to the application of quantitative techniques in all areas of baseball. In this paper, we apply Discriminant Analysis to the selection problem faced by the Baseball Hall of Fame members and analyze the performance of the process since its inception. 2 - Graphical Lasso And Thresholding: Conditions For Equivalence Somayeh Sojoudi, University of California, Berkeley, 1543 Delaware Street, Berkeley, CA, 94703, United States, somayeh.sojoudi@gmail.com Graphical lasso is a popular technique for finding a sparse inverse covariance matrix from a small number of samples. Graphical lasso is computationally expensive for large-scale problems due to a positive semidefinite constraint. A cheap heuristic method for finding a graphical model is to simply threshold the sample correlation matrix. By introducing the notions of sign-consistent and inverse-consistent matrices, we derive sufficient conditions under which graphical lasso and thresholding produce the same solution. These conditions are expected to be satisfied for sufficiently sparse graphical models. We test the conditions on electrical circuits and functional MRI data. 3 - Quantitative Compliance As A Driver For Automation Leif Meier, Professor, University of Applied Sciences Bremerhaven, An der Karlstadt 8, Bremerhaven, 27568, Germany, lmeier@hs-bremerhaven.de Compliance management covers all efforts to comply with regulations such as laws and rules, policies and standards. Automated processes are dealing with a huge number of (trans-) actions to be executed in short term, depending on big data sets. Each single transaction that is executed must comply with regulations and must be transparent to auditors. Quantitative Compliance provides methods to manage processes and risks in complex systems considering regulations to improve decisions from available information. We provide an example to identify risks from Anti-Money-Laundering (AML) in Financial Transactions and show applications of this approach to data-driven systems in new areas.

WA01 101A-MCC Forecasting Sponsored: Data Mining Sponsored Session

Chair: Ivan G Guardiola, Missouri S&T, 600 W. 14th St, Rolla, MO, 65409, United States, guardiolai@mst.edu 1 - Ensemble Methods With Disparate Data Sources For Stock Market Prediction Lin Lu, Auburn University, Auburn, AL, United States, lzl0032@auburn.edu, Bin Weng, Fadel Mounir Megahed Stock market has time critical characteristics which draws attentions from both investors and researchers. The objective of this study is to develop a prediction model for stock’s short-term movement forecasting. We assume more related data sources used will increase the prediction performance. In this study, we consider data from Wikipedia, Financial news, Market sentiment and Stock market history data. Different features are generated from these data sources and data mining methods are applied to select the most important ones. Next, ensemble methods are used to develop the model. As a result, our prediction model dominates related studies for the stock market forecasting. 2 - Occupancy Level Analysis At A VA Hospital That Considers Discharge Of Patient Medical Decisions Ivan G Guardiola, Associate Professor, Missouri S&T, 600 W. 14th St, Rolla, MO, 65409, United States, guardiolai@mst.edu, Tatiana Cardona The improvement of short-term information is vital to obtain positive gains in various hospital operational and business processes. To this end, the prediction or forecasting of hospital census gives insight into hospital resource use that results in better planning. This paper presents a combination of nonparametric and parametric models to deal with the intra-week seasonality from the daily discharge distribution. 3 - Neural Networks Based Linear Ensemble Framework for Time Series Forecasting Lin Wang, Huazhong University of Science and Technology, Wuhan, China, Zhigang Wang In this study, a combination forecasting model resulting from a novel ensemble framework of four neural networks is proposed for time series forecasting. The proposed framework has two primary advantages: (a) a heuristic to determine the number of input and hidden neurons for each neural network, and (b) a BPNN-BSA based mechanism for the associated combining weights. Both of the advantages will improve the accuracy of each individual model and the final lin- ear combination modle. Experimental results performed on nine time series datasets show that the ensemble framework outperforms the component neural network models and other well recognized models.

WA02 101B-MCC Data Mining in Healthcare 1 Sponsored: Data Mining Sponsored Session

Chair: Adel Alaeddini, University of Texas at San Antonio, Department of Mechanical Engineering, One UTSA Circle, San Antonio, TX, 78249, United States, adel.alaeddini@utsa.edu Co-Chair: Anh Pham, University of Arkansas, 1411 S Washington Avenue, Fayetteville, AR, 72701, United States, anh.pham1234@gmail.com 1 - Using Data Mining To Detect Fraud And Abuse Under National Health Insurance System In China Chong Li, Beijing Institute of Technology, Beijing, 100081, China, lichongbit@163.com, Zihao Jiao, Huijuan Cao Health care fraud and abuse are pressing problems, causing an important fraction of total health expenditure wasted. Data mining methods can be used to automatically detect fraud in billions insurance claim data, superior to the time- consuming and practically efficient traditional auditing methods. Nevertheless, few studies have been dedicated to this field in China. This paper presents how to apply unsupervised methods to extract useful information and identify a smaller subset from the claims for further assessment under China National Health Insurance system. Our approach will help in streamlining auditing approaches towards the suspect groups rather than routine auditing of all claims.

363