Informs 2015

78 / 552

INFORMS Philadelphia – 2015
76
3 - A Structural Approach to Community Detection in
Complex Networks
Song Chew, Associate Professor, Southern Illinois University
Edwardsville, Southern Illinois University-Edwardsville,
Edwardsville, IL, 62026, United States of America,
We in this study develop a novel measure of community structure that gauges the
strengths and weaknesses of a proposed community structure against an ideal. In
addition, we present an algorithm that may, as it maximizes our measure, return
several alternative community structures for consideration. We provide several
examples to demonstrate use of our measure, and to illustrate applications of our
algorithm as well.
SB33
33-Room 410, Marriott
Applications of Markov Models to Medical Decision
Making Problems
Sponsor: Health Applications
Sponsored Session
Chair: M. Reza Skandari, University of British Columbia, Vancouver,
Vancouver, Canada,
1 - Evaluation of Breast Cancer Mammography Screening Policies
Considering Adherence Behavior
Maboubeh Madadi, University of Arkansas, ,
Shengfan Zhang, Louise Henderso
The efficacy of mammography screening guidelines is highly associated with
women’s compliance with these recommendations. Currently, none of the
existing policies take women’s behavior into consideration. In this study, we
develop a randomized partially observable Markov chain model to evaluate a
wide range of screening mammography policies, incorporating heterogeneity in
women’s adherence behaviors.
2 - Policy Approximation for Optimal Treatment Planning
Wesley Marrero, University of Michigan, 500 South State Street,
Ann Arbor, MI, 48109, United States of America,
, Mariel Lavieri, Jeremy B. Sussman,
Greggory J. Schell, Rodney A. Hayward
Markov decision process (MDP) models are powerful tools which enable the
derivation of optimal treatment policies, but may incur long computational times
and decision rules which are challenging to interpret by physicians. To reduce
complexity and enhance interpretability, we study how Poisson regression may be
used to approximate optimal hypertension treatment policies derived by a MDP
for maximizing a patient’s expected discounted quality-adjusted life years.
3 - Optimal Decision Making in a Markov Model with Parameter
Uncertainty: The Case of CKD
M. Reza Skandari, University of British Columbia-Vancouver,
Vancouver, BC, Canada,
Steven Shechter, Nadia Zalunardo
We investigate a Markov decision process whose unknown transition parameters
are revealed partially through state observation. Decisions are made as the state
evolves. We use the model to study the optimal time to start preparing a type of
vascular access for chronic kidney disease patients who will need dialysis.
4 - Reinforcement Learning Algorithm for Blood Glucose Control in
Diabetic Patients
Mahsa Oroojeni Mohammad Javad, Northeastern University, 334
Snell Engineering, Northeastern Univ, Boston, United States of
America, , Stephen Agboola,
Kamal Jethwani, Ibrahim Zeid, Sagar Kamarthi
In this paper a reinforcement learning algorithm is proposed for regulating the
blood glucose level of Type I diabetic patients. In the proposed reinforcement
learning algorithm body weight and A1C level define the state of a diabetic
patient. For the agent, insulin dose levels constitute the actions. As a result of a
patient’s treatment, after each time step t, the patient receives a numerical reward
depending on the response of the patient’s health condition.
SB34
34-Room 411, Marriott
Smart Medical Prognosis and Decision Making
via Data Mining
Sponsor: Health Applications
Sponsored Session
Chair: Danica Xiao, PhD Candidate, University of Washington, Seattle,
3900 Northeast Stevens Way, Seattle, WA, 98195, United States of
America,
Co-Chair: Shouyi Wang, Assistant Professor, University of Texas at
Arlington, 3105 Birch Ave, Grapevine, TX, 76051, United States of
America,
1 - Does Specialization of Health Care Services Increase
Operational Efficiency?
Saied Samiedaluie, Postdoctoral Fellow, University of British
Columbia, E204 - 4500 Oak Street, BC Women’s Hospital,
Women’s Health Rese, Vancouver, BC, V6H 3N1, Canada,
, Vedat Verter
We study a health care network configuration problem considering two scenarios:
specialization versus generalization. We characterize the settings in which each
scenario is preferred in terms of minimizing the patient admission refusal rate.
Our results show that the decision of system configuration for a multi-hospital
network requires careful consideration of patient mix among arrivals, relative
length of stay of patients, and distribution of patient load between hospitals.
2 - A Model to Predict Depression among Diabetes Patients with
Application in Screening Policymaking
Haomiao Jin, University of Southern California, 3715 McClintock
Ave, GER 240, Los Angeles, CA, United States of America,
Shinyi Wu
About 30% of diabetes patients are suffering from depression, but nearly half of
them are undiagnosed. Universal screening improves depression diagnosis rates
but is labor-intensive. A machine learning model is developed to predict
depression among diabetes patients. The model is applied in a screening policy to
help healthcare providers to better prioritize the use of their resources and time
and increase efficiency in managing their patient population with depression.
3 - Data Mining Techniques Applied to the Study of Canines
with Disease
Zhenpeng Miao, Saint Joseph’s University, 5600 City Ave,
Philadelphia, PA, 19131, United States of America,
Yingdao Qu, Virginia Miori
This paper, the third in a series, aims at providing models effective in predicting
the degree of pain and discomfort in canines suffering from osteoarthritis,
sarcoma, dermatitis and side effects of radiation treatments. The R programing
language and SAS JMP are used to clean data and execute multivariate analyses
to predict changes in different activity levels. The predictive models provide
information that can assist in effective diagnosis and medication of suffering dogs.
4 - Understanding Linear and Non Linear Brain Dynamics During
Manual Lifting Tasks
Awad Aljuaid, PhD Student, UCF, University of Central Florida
4000 Cent, Department of Industrial Engineering, Orlando, FL,
32816-2993, United States of America, ,
Waldemar Karwowski, Petros Xanthopoulos
The aim of this study is to test the change on different EEG measures during
various psychophysical lifting frequencies. High-density wireless dry cell EEG
device have been used to record brain signals. Twenty healthy males participated
in this experiment performing two physical lifting sessions psychophysical weight
lifting (low, medium, and high) and strength measurements (Isometric and
isokinetic). EEG recording at different brains locations are analyzed with linear
and non-linear methods.
SB33