2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
456
Wednesday, 2:45PM - 4:15PM
WD01
101A-MCC
Data Mining Application in Business
Sponsored: Data Mining
Sponsored Session
Chair: Parvaneh Jahani, University of Louisville,
781 Theodore Burnett Court, Apt 2, Louisville, KY, 40217,
United States,
1 - Machine Learning And Cognitive Pricing
Zhengliang Xue, IBM Research Center, 1101 Kitchawan Road,
Route 134, Yorktown Heights, NY, 10598, United States,
Markus Ettl
We study a method to price personalized configuration of software and services.
The seller has to deal with a customized configuration without any similar records
in history. A data-driven approach is applied to estimate the purchase probability
for any unique configuration based on historical trading data. In addition, client
relationship and firmographic information need to be incorporated to the pricing
decision. We establish a utility model to evaluate the configuration and recognize
the impact of relationship. The business impact of optimal pricing is justified by
the actual data.
2 - Investigating Sparse Demand Models To Support The Assortment
Planning Decision
Matthew Lanham, Purdue University, West Lafayette, IN, 47905,
United States, , Ralph D Badinelli
We present research examining the performance of substitution-based multi-
classification models currently being researched and employed in practice by
major retailers, versus more naïve binary classification models to understand
purchase propensity. We discuss how these models would yield different
assortments for sparse demand products.
WD02
101B-MCC
Decision Analysis in Health Care Data Mining
Sponsored: Data Mining
Sponsored Session
Chair: Diego Martinez, Johns Hopkins University, 1, Baltimore, MD,
212, United States,
1 - Multiscale Decision Making Based on Variational Evidential
Reasoning For Medical Record Label Recommendation
Haiyan Yu, Lecturer, Chongqing University of Posts and
Telecommunications, 2 Chongwen Road, Nan’an District,
Chongqing, P.R.C, 400065, China,
Haiyan Yu, Lecturer, University of Electronic Science and
Technology of China, Chengdu, China, ,
Man Xu, Jiang Shen
Due to the characteristics of fragmentation and uneven distribution of the clinical
data, the issues of reducing its misdiagnosis and improving the system
discrimination ability arises in evidential reasoning. To solve these issues, the
model of belief propagation was constructed based on the hierarchical Dirichlet
process, achieving the confidence fusion of fragmented evidence. Through
parameter learning, the recommendation of indentifying the lables of medical
instances were achieved with the control trajectory of the reasoning model.
Finally, simulation study verified the effectiveness and resilience of the reasoning
models, improving the efficiency and quality of medical services.
2 - Using Matrix-based Multi-criteria Decision Method For Assessing
Risk Of Harm Of Alert-overridden Intravenous Infusions
Wan-Ting Su, PhD Student, Purdue Univeristy, West Lafayette, IN,
United States, , Poching DeLaurentis,
Mark Lehto
Hospital medication safety teams regularly review and analyze infusion drug alert
reports to evaluate infusion performance. Previously an Intravenous (IV)
medication harm index was developed to help clinicians assess the potential
patient harm of each alert-overridden infusion. We aim to apply a matrix-based
multi-criteria decision method to improve the existing harm index. The improved
index can help medication safety teams better identify the medication and care
unit combinations of high risk and further prioritize the targets for improvement
on nursing practice, workflows and drug limit settings.
3 - Control System For Electronic Triage In The Emergency
Department: Integrating The User Into Development Loop
Diego A. Martinez, Johns Hopkins School of Medicine, Baltimore,
MD, 21201, United States, , Scott R Levin
The potential for machine learning systems to improve via exchange of
information with knowledgeable users has yet to be explored in much detail. In a
pilot study in an emergency department of a large hospital, nurses were presented
with triage level predictions, and they were able to provide feedback through a
real-time communication system. The types of some of this feedback seem
promising for assimilation of clinical gestalt by machine learning systems. The
results show that to benefit from clinical gestalt; machine learning systems must
be able to absorb information in a graceful manner and provide clear explanations
of their predictions.
WD03
101C-MCC
Big Data IV
Contributed Session
Chair: Wenbo Sun, University of Michigan, 2013 Medford Rd Apt 161,
Ann Arbor, MI, 48104, United States,
1 - A Revaluation Of The Relationship Between Environmental
Management And Financial Performance – A Multilevel
Longitudinal Analysis
Zuoming Liu, Lynchburg College, 1501 Lakeside Drive, School of
Business & Economics, Lynchburg, VA, 24501, United States,
This study employs a multilevel cross-lagged model to investigate the causal
relationships between a corporation’s environmental performance and financial
returns by using a 4-year dataset of the largest US500 companies. The goal is to
identify variation of relationship due to different features at various levels. By
conducting multilevel analyses, the relationship between environmental
performance and financial returns is demystified into three levels, intra-firm
dynamic variations over time, inter-firm variations, as well as variations across
industries.
2 - A Class Of First Order Methods That Do Not Rely On Any Norm
Haihao Lu, PhD Student, MIT, 60 Wadsworth St, Apt 8B,
Cambridge, MA, 02142, United States, ,
Yurii Nesterov, Robert Michael Freund
This work generalizes the notion of smoothness, strong convexity, and Lipschitz
continuity of a convex function by introducing a reference function, and uses the
reference function to derive convergence rates for generalized first-order methods
for convex optimization. The approach yields clear intuition behind convex
optimization with composite functions as a corollary. We also developed a first-
order interior-point method using a weak definition of self-concordance.
3 - A Method For Developing Confidence Bands For Multiple
Dimensional Functional Responses
Wenbo Sun, University of Michigan, 2013 Medford Rd Apt 161,
Ann Arbor, MI, 48104, United States, ,
Jionghua Jin
The standard method for specifying target responses’ variabilities involves
developing a confidence band for a set of empirical mechanical responses. These
responses are multiple-dimension signals obtained from identical trials of different
subjects. The existing methods commonly normalize responses with respect to
subject characteristics, point-wisely generate confidence bands ignoring times and
direction’s correlation. A new method was developed in the structure of mixture
models based on basis-representation and Gaussian process and provided an
approach for outlier detection. It is applied to the kinematic response data
collected in Children’s Hospital of Philadelphia.
WD01