Informs 2015

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INFORMS Philadelphia – 2015
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3 - Using Linear Programming Based Exploratory Techniques in Gene
Expression Consensus Clustering
Victoria Ellison, North Carolina State University, Campus Box
7913, 2500 Stinson Drive, Raleigh, NC, 27695, United States of
America, , Yahya Fathi, Amy Langville
We propose a divisive hierarchical consensus clustering algorithm (DHCCA) by
modifying a BILP formulation of the Median Partition problem and applying
several proposed parametric programming algorithms based on the optimal
partition parametric programming algorithm. We prove that executing our
DHCCA, under certain assumptions, is equivalent to solving the minimum ratio
cut problem. This equivalency can be useful in creating fast heuristics to the
Median Partition problem.
4 - Using Data Mining to Predict Drug Courts Outcome
Hamed Majidi Zolbanin, Oklahoma State University, 309 S. West
St, Unit 6, Stillwater, Ok, 74075, United States of America,
, Durand Crosby, Dursun Delen
Drug court is an alternative for traditional criminal courts that attempts to shift
from a punitive to a therapeutic jurisprudence. Under this new philosophy, the
eligible offenders are held as individuals in need of rehabilitative treatments. The
initiative is proved to be effective in lowering the costs and breaking the cycle of
narcotics use. To better manage the resources and maximize the benefits, this
study develops a model to predict who will or will not graduate from these courts.
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33-Room 410, Marriott
Methods and Applications in Disease Detection and
Treatment
Sponsor: Health Applications
Sponsored Session
Chair: Shan Liu, Assistant Professor, University of Washington,
Seattle, WA, United States of America,
1 - Sequencing Chemotherapy Agents for Metastatic Colorectal
Cancer Patients
Lakovos Toumazis, PhD Candidate, Department of Industrial and
Systems Engineering, University at Buffalo, SUNY, Buffalo,
United States of America, , Artemis
Toumazi, Loukia Karacosta, Daniel A. Goldstein, Changhyun
Kwon, Murat Kurt
Colorectal cancer is the third most lethal cancer in the US affecting both genders.
Despite advancements in chemotherapy treatment, long-term survival for the
advanced stage of the disease remains poor. With the goal of improving the
effectiveness of chemotherapy treatment for metastatic colorectal cancer patients
we developed a Markov decision process model that jointly optimize the duration
and sequence of the available drugs. The obtained optimal policy improves
survival by at least 6 months.
2 - Large-scale Personalized Health Surveillance by
Selective Sensing
Ying Lin, University of Washington, Box 352650, Seattle, WA,
98195-2650, United States of America, ,
Shan Liu, Shuai Huang
Detecting subjects who have been on the trajectory towards disease onset holds
promises for preventative healthcare. Development of personalized health
surveillance is enabled by sensing and information technologies. To scale up
personalized surveillance, we developed a selective sensing method that
integrates degradation modeling, prognosis, and optimization, which can cost-
effectively monitor a large number of individuals by exploiting the similarities of
their disease trajectories.
3 - Cost Effectiveness of Expanding Anti-retroviral Therapy to
Untreated Subpopulations in Botswana
Thomas Keller, University of Minnesota, 111 Church St. SE,
Minneapolis, MN, 55455, United States of America,
, Gregory Bisson, Diana Negoescu,
Daniel Winetsky
In 2002 the Botswana government initiated Africa’s first national anti-retroviral
program, which has enrolled over 200,000 Batswana HIV-infected patients since
its inception. We develop a dynamic compartmental model to evaluate the cost
effectiveness of expanding this program to serve more Batswana citizens as well
as migrant workers, who constitute a significant proportion of HIV-infected
patients.
4 - Minimizing Overdiagnosis in Cancer Screening
Maboubeh Madadi, University of Arkansas, ,
Shengfan Zhang, Edward Pohl, Chase Rainwater
Overdiagnosis is defined as the diagnosis of screen-detected cancers that would
not have presented clinically in a woman’s lifetime in the absence of screening.
Overdiagnosis is known to be the most important disadvantage of cancer
screening and it can adversely affect people’s lives. In this study a mathematical
programming model is developed to find the optimal cancer screening policy with
respect to overdiagnosis risk, while maintaining the lifetime cancer mortality risk
at a low threshold.
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34-Room 411, Marriott
Medical Decision Making
Sponsor: Health Applications
Sponsored Session
Chair: Pooyan Kazemian, PhD Candidate, University of Michigan-
Ann Arbor, 1205 Beal Ave., Ann Arbor, MI, 48105,
United States of America,
1 - Why is Screening so Common for Some Diseases when
Evidence is so Uncertain?
Ozge Karanfil, PhD Candidate, MIT Sloan School of Management,
100 Main Street, E62-379, Cambridge, MA, 02142,
United States of America, John D. Sterman
Practice guidelines for routine screening such as mammography or PSA testing
have changed significantly over time. Evidence-based guidelines are often not
followed by clinicians and patients, with significant over or under screening. In
this study we describe a dynamic model to explain changes in policy action
thresholds. We use quantitative and qualitative data to document evidence of
gaps between guidelines and practice. Qualitative data includes interviews with
health/medical professionals.
2 - Planning for HIV Screening, Testing, and Care at the Veteran’s
Health Administration
Kumar Rajaram, Professor of Decisions, Operations, and
Technology Management, UCLA Anderson School of
Management, B410 Gold Hall, UCLA Anderson, Los Angeles, CA,
90024, United States of America, ,
Matthew Goetz, Uday Karmarkar, Sandeep Rath, Sarang Deo
CDC has recommended a routine screening policy for HIV. We modeled a QALY
maximizing nonlinear mixed integer program incorporating system dynamics and
disease progression and found that routine screening may not be always feasible.
We applied this model to the Greater Los Angeles station of the Veterans Health
Administration and used it to develop and evaluate managerially relevant policies
within existent capacity and budgetary constraints to improve upon the current
screening policy.
3 - Value of Patient-centric Treatment Policies for Pelvic Organ
Prolapse in Women
Yueran Zhuo, Ph.D. Candidate, University of Massachusetts
Amherst, Isenberg School of Management, Amherst, MA, 01003,
United States of America, , Senay Solak
Pelvic organ prolapse (POP) is a common condition that impacts many women’s
health and quality of life. The selection of a treatment option for POP depends on
several factors, but personal preferences of the patient play a more significant role
than most other similar conditions. We specifically take this aspect of POP into
account and identify patient-centric treatment recommendations for this
syndrome. The value of these policies is then assessed through comparisons with
physician decisions.
4 - On Low-cost In-home Sensor Placement for Personalized
Tracking of Activity of Older Adults
Alexander Nikolaev, Assistant Professor, University at Buffalo
(SUNY), 312 Bell Hall, Buffalo, NY, 14260-2050,
United States of America, , Ann Bisantz,
Siddhartha Nambiar, Melissa Green, Lora Cavuoto
Activity-tracking sensors can now be used for personalized care; yet, challenges
with usability and cost may prevent their adoption by the elderly. We assess how
low-cost in-home sensor systems help infer physical activity levels of tenants. The
American Time Use Survey and real apartment layouts are used to study optimal
sensor count, placement, and activity prediction error levels. We describe sensor
placement strategies and apartment layouts best suited for sensor technology use.
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