Informs 2015

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INFORMS Philadelphia – 2015
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TD29
29-Room 406, Marriott
Joint Session Analytics/HAS:The Emerging Role of
Health Systems Engineering and its Impact on
Clinical Informatics and Analytics
Sponsor: Analytics
Sponsored Session
Chair: John Zaleski, Chief Informatics Officer, Nuvon, Inc.,
4801 S. Broad Street, Suite 120, Philadelphia, PA, 19112,
United States of America,
1 - How to Make Clinically Actionable Alarms
Jeanne Venella Dnp, Chief Nursing Officer, Nuvon,
4801 S Broad St, Philadelphia, PA, 19112,
United States of America,
How to Make Clinically Actionable Alarms The very alarm systems that were
created to enhance patient safety have themselves become an urgent patient
safety concern. We need to fix our current state of alarm systems. We must
achieve both a higher level of sensitivity and specificity. Therefore; reducing both
false and non-actionable alarms. Our goal is ignite the talk on alarm fatigue, begin
to define algorithms for smarter actionable alarms and provide a safer health care
environment.
2 - The Kalman Filter and its Application to Real-time Physiologic
Monitoring of High-acuity Patients
John Zaleski, Chief Informatics Officer, Nuvon, Inc.,
4801 S. Broad Street, Suite 120, Philadelphia, PA, 19112,
United States of America,
The Kalman Filter (KF) has seen application in many fields owing to its rapid
computational framework and intrinsic optimality in tracking time-series. In this
presentation, the KF is used to optimally track and smooth signal artifact
associated with patient physiologic monitoring.
3 - Predicting the Effect of Introducing Walk in Hours on Staff
Workload at a Pediatrics Practice
Saurabh Jha, University of Pittsburgh, 1048 Benedum Hall,
Department of Industrial Engineering, Pittsburgh, PA, 15261,
United States of America, Louis Luangkesorn,
Diana Hoang, Lindsey Jones, Tricia Pil
A local pediatrics practice has introduced patient walk-in hours in response to
competition from urgent care clinics and has asked to determine the effect on staff
workload. We evaluate the effect of walk-in hours on the practice workload, then
develop and validate a predictive model for the various types of visits and phone
calls. After validating the predictive model, we develop a forecast for the
remainder of the 12 months period following the introduction of all-day walk-ins.
TD30
30-Room 407, Marriott
Decision Support Systems II
Contributed Session
Chair: Rohit Nishant, Assistant Professor, ESC Rennes School of
Business, 2 rue Robert d’Arbrissel CS 76522, Rennes, 35065, France,
1 - Routing Recommendation System for Uber
Yuhan Wang, University of California Irvine, 6478 Adobe Cir,
Irvine, CA, 92617, United States of America,
Paper not available at this time.
2 - Optimising Allocation of Investor Funds in Multi-objective Public
Infrastructure Investment Programs
Martin Spollen, Queens University Belfast, David Bates Building,
University Road, Belfast, BT7 1NN, United Kingdom,
This session will examine the development and application of Strategic
Infrastructure Planning Models (SIPMs) as an emerging class of investment
appraisal techniques for public investment management. The techniques focus
attention on the network effects of investment on total portfolio performance.
Application to a major regional schools investment and rationalization program is
demonstrated.
3 - A Decision Support System for Traffic Diversion around
Construction Closures
Arezoo Memarian, Graduate Research Assistant, University of
Texas at Arlington, 425 Nedderman Hall, 416 Yates St.,
Box 19308, Arlington, TX, 76019, United States of America,
Siamak Ardekani
The objective of this study is to develop a PC-based decision support tool with a
user-friendly graphical interface to allow development of optimal traffic
management plans around highway construction sites. In addition to the
capability to identify optimum traffic diversion routes, such a tool would also
allow simulation of various traffic management plan scenarios envisioned by
experts.
4 - Can Virtualization Maturity Impact Software Development Project
Performance: An Empirical Study
Rohit Nishant, Assistant Professor, ESC Rennes School of
Business, 2 Rue Robert d’Arbrissel CS 76522, Rennes, 35065,
France, , Bouchaib Bahli
In this article we invoke IT asset classes’ taxonomy and IT capability maturity
model to examine the impact of virtualization maturity on software development
project performance. Our findings suggest that virtualization capability has a
distinct impact on software development project and process performance.
Finally, this study extends virtualization maturity model’s validity. Implications
for research and practice are discussed.
TD31
31-Room 408, Marriott
Time Series Data Mining
Sponsor: Data Mining
Sponsored Session
Chair: Mustafa Gokce Baydogan, Assistant Professor, Bogaziai
University, Department of Industrial Engineering, Bebek, Istanbul,
34342, Turkey,
1 - On the Parameter Identification of a New Knot Selection
Procedure in Mars
Cem Iyigun, Associate Professor, Middle East Technical
University, ODTU Kampusu Endustri Muhendisligi Bolum,
Oda 331 Cankaya, Ankara, 06801, Turkey, ,
Elcin Kartal Koc
Multivariate Adaptive Regression Splines (MARS) is a popular nonparametric
regression for estimating the nonlinear relationship within data via piecewise
functions. A clustering based knot selection method has been proposed to the
literature recently. This study proposes a parameter selection criteria based on
Schwarz’s Bayesian Information for determining the optimum grid size and
threshold value of this new procedure. Numerical studies are conducted via
artificial and real datasets.
2 - Discovering Interpretable Nonlinear Variation Patterns in
High-Dimensional Data over Spatial Domains
Phillip Howard, Arizona State University, 699 S Mill Ave, Tempe,
AZ, 85281, United States of America,
Daniel Apley, George Runger
The objective of this research is the identification of distinct and interpretable
nonlinear variation patterns in high-dimensional data through dimensionality
reduction. We present a new method for learning reduced dimension
representations which characterize interpretable variation sources when mapped
to the original feature space. A new metric for measuring how well the solution
can be interpreted is also proposed. We compare our work to alternative methods
using several examples.
3 - EEG Signal Classification using Functional Principal
Component Analysis
Woo-Sik Choi, Mr., Korea University, 145, Anam-Ro, Seongbuk-
Gu, Innovation Hall, 816, Korea University, Seoul, Korea,
Republic of, , Seoung Bum Kim
Electroencephalogram (EEG) is recordings of the electrical potentials of the brain.
Identifying human activities from EEG is the main goal of brain computer
interface area. To analyze events, selecting important features is a crucial step. In
this study, we propose a feature extraction using functional principal component
analysis with general classification methods. The effectiveness of the proposed
method is demonstrated through a real data from the brain computer interface
competition 2003.
TD31