Health

G Inferring the risk factors behind the geographical spread nd transmission of Zika in the Americas

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Lauren M. Gardner , András Bóta, Karthik Gangavarapu, Moritz U. G. Kraemer, Nathan D. Grubaugh ( 2018 )

Inferring the risk factors behind the geographical spread
nd transmission of Zika in the Americas

PLoS neglected tropical diseases
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Tak A predictive model of rats’ calorie intake as a function of diet energy density

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Rahmatollah Beheshti, Yada Treesukosol, Takeru Igusa, Timothy H Moran ( 2018 )

A predictive model of rats’ calorie intake as a function of diet energy density

American Journal of Physiology-Regulatory, Integrative and Comparative Physiology
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S Evaluating Nurse Staffing Levels in Perianesthesia Care Units Using Discrete Event Simulation

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Siddiqui, S., Morse, E., Levin, S., ( 2017 )

Evaluating Nurse Staffing Levels in Perianesthesia Care Units Using Discrete Event Simulation

IISE Transactions on Healthcare Systems Engineering, 7(4): 215-223.

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Tak Improving health systems performance in low- and middle-income countries: a system dynamics model of the pay-for-performance initiative in Afghanistan

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O Alonge, S Lin, T Igusa, DH Peters ( 2017 )

Improving health systems performance in
low- and middle-income countries: a system
dynamics model of the pay-for-performance
initiative in Afghanistan

Health Policy and Planning
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Tak Examining the structure and behavior of Afghanistan’s routine childhood immunization system using system dynamics modeling

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Holly B Schuh, Maria W Merritt, Takeru Igusa, Bruce Y Lee, David H Peters ( 2017 )

Examining the structure and behavior of Afghanistan’s routine childhood immunization system using system dynamics modeling

International Journal of Health Governance
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Tak Taking dietary habits into account: A computational method for modeling food choices that goes beyond price

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Rahmatollah Beheshti, Jessica C Jones-Smith, Takeru Igusa ( 2017 )

Taking dietary habits into account: A
computational method for modeling food
choices that goes beyond price

PloS one
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In radiation therapy with continuous dose delivery for Gamma Knife® Perfexion™, the dose is delivered while the radiation machine is in movement, as oppose to the conventional step-and-shoot approach which requires the unit to stop before any radiation is delivered. Continuous delivery can increase dose homogeneity and decrease treatment time. To design inverse plans, we first find a path inside the tumor volume, along which the radiation is delivered, and then find the beam durations and shapes using a mixed-integer programming optimization (MIP) model. The MIP model considers various machine-constraints as well as clinical guidelines and constraints.

Radiation therapy is frequently used in diagnosing patients with cancer. Currently, the planning of such treatments is typically done manually which is time-consuming and prone to human error. The new advancements in computational powers and treating units now allow for designing treatment plans automatically.

To design a high-quality treatment, we select the beams sizes, positions, and shapes using optimization models and approximation algorithms. The optimization models are designed to deliver an appropriate amount of dose to the tumor volume while simultaneously avoiding sensitive healthy tissues. In this project, we work on finding the best beam positions for the radiation focal points for Gamma Knife® Perfexion™, using quadratic programming and algorithms such as grassfire and sphere-packing.

Assume that a decision-maker’s uncertain behavior is observed. We develop a an inverse optimization framework to impute an objective function that is robust against misspecifications of the behavior. In our model, instead of considering multiple data points, we consider an uncertainty set that encapsulates all possible realizations of the input data. We adopt this idea from robust optimization, which has been widely used for solving optimization problems with uncertain parameters. By bringing robust and inverse optimization together, we propose a robust inverse linear optimization model for uncertain input observations. We aim to find a cost vector for the underlying forward problem such that the associated error is minimized for the worst-case realization of the uncertainty in the observed solutions. That is, such a cost vector is robust in the sense that it protects against the worst misspecification of a decision-maker’s behavior. 

As an example, we consider a diet recommendation problem. Suppose we want to learn the diet patterns and preferences of a specific person and make personalized recommendations in the future. The person’s choice, even if restricted by nutritional and budgetary constraints, may be inconsistent and vary over time. Assuming the person’s behavior can be represented by an uncertainty set, it is important to find a cost vector that renders the worst-case behavior within the uncertainty set as close to optimal as possible. Note that the cost vector can have a general meaning and may be interpreted differently depending on the application (e.g., monetary cost, utility function, or preferences). Under such a cost vector, any non-worst-case diet will thus have a smaller deviation from optimality.  

We introduce a new approach that combines inverse optimization with conventional data analytics to recover the utility function of a human operator. In this approach, a set of final decisions of the operator is observed. For instance, the final treatment plans that a clinician chose for a patient or the dietary choices that a patient made to control their disease while also considering her own personal preferences. Based on these observations, we develop a new framework that uses inverse optimization to infer how the operator prioritized different trade-offs to arrive at her decision. 

We develop a new inverse optimization framework to infer the constraint parameters of a linear (forward) optimization based on multiple observations of the system. The goal is to find a feasible region for the forward problem such that all given observations become feasible and the preferred observations become optimal. We explore the theoretical properties of the model and develop computationally efficient equivalent models. We consider an array of functions to capture various desirable properties of the inferred feasible region. We apply our method to radiation therapy treatment planning—a complex optimization problem in itself—to understand the clinical guidelines that in practice are used by oncologists. These guidelines (constraints) will standardize the practice, increase planning efficiency and automation, and make high-quality personalized treatment plans for cancer patients possible.