2016 INFORMS Annual Meeting Program

SD19

INFORMS Nashville – 2016

SD19 106B-MCC Parallel Computing for Optimization and Data Analysis Sponsored: Computing Sponsored Session

SD21 107A-MCC Healthcare, General Contributed Session Chair: Julie Lynn Hammett, Texas A&M University, 301 Holleman Dr E, Apt 728, College Station, TX, 77840, United States, jhammett@tamu.edu 1 - Analysis Of Physician Productivity In Emergency Departments Krista Foster, University of Pittsburgh, Mervis Hall, Roberto Clemente Drive, Pittsburgh, PA, 15260, United States, kmf88@pitt.edu, Jennifer S Shang We present our analysis of a cohort of U.S. emergency departments. We use visit- level data to analyze hospital processes and develop models for physician productivity. 2 - A Review And Extension Of Clinically Significant, Automated Estimation Of End Systolic And End Diastolic Volumes In Cardiac MRIs Michael Kim, Booz Allen Hamilton, 3930 Valley Ridge Drive, Fairfax, VA, 22033, United States, mikeskim@gmail.com We review the winning methods in Kaggle’s Second Annual Data Science Bowl. The top three algorithms automatically measure endsystolic and enddiastolic volumes in cardiac MRIs using data from more than 1000 patients. The results were found to be clinically significant. An analysis of the winning solutions is presented with a focus on extension through ensembling and transfer learning. In particular, we architect a machine learning pipeline to extend the top algorithms to the case of cancer detection given a time series of prostate MRIs. 3 - The Risks Of Risk Adjusted Mortality Rates And A Proposed Alternative Measure Thomas Raymond Sexton, Professor, Stony Brook University, 317 Harriman Hall, Stony Brook, NY, 11794-3775, United States, Thomas.Sexton@StonyBrook.edu, Christine Pitocco We consider the widespread use of the risk-adjusted mortality rate (RAMR) to evaluate hospital performance. We demonstrate that the RAMR, as currently employed, has significant methodological flaws. We propose an alternative to the RAMR that is based on standard statistical theory and methods. Applying our measure yields a more complete and accurate evaluation of hospitals. 4 - Effects Of Artificial Agents Based Ordering On The Supply Chain Of Perishables Harshal Lowalekar, Assistant Professor, Indian Institute of Management-Indore, Prabandh Shikhar, Rau-Pithampur Road, Indore, 453331, India, lwlherschelle@gmail.com, Raghu Santanam, Ajay Vinze We develop a blood bank game which contains a mix of human and computer based hospital blood banks who order blood at regular intervals from a regional bank. The objective of all the agents is to minimize their total inventory costs. The computer agents use a near-optimum policy to determine their order sizes. We show that presence of a large number of computer based agents in the supply chain leads to a systematic increase in the order sizes of the hospital banks which leads to a severe perceived blood shortage in the region. The performance of the supply chain worsens when the computer agents have the capability to learn from their past performance. 5 - Remote Patient Monitoring System Framework: A User Perspective Julie Lynn Hammett, Texas A&M University, 301 Holleman Dr E, Apt 728, College Station, TX, 77840, United States, jhammett@tamu.edu, Michelle M. Alvarado, Mark Alan Lawley Healthcare providers are facing an increasing number of patients requiring long- term care, introducing new challenges to providing fast and affordable care. We present ongoing research to create a framework for the design, development, and implementation of remote patient monitoring (RPM) for chronic care. We highlight the stakeholder needs, system requirements and component interdependencies. We describe RPM’s need for automated solutions that support clinical decisions and deliver interventions. This technology must be interchangeable to suit the varied needs and characteristics of many patients. We show that these solutions can improve chronic care management.

Chair: Jonathan Eckstein, Rutgers University, Rutgers University, Piscataway, NJ, 00000, United States, jeckstei@rci.rutgers.edu 1 - The Rectangular Maximum Agreement Problem And Its Data Analysis Applications Ai Kagawa, Rutgers University, ai.kagawa@gmail.com The NP-hard rectangular maximum agreement (RMA) problem finds a “box” that best discriminates between two weighted datasets. Its data analysis applications include boosting classification methods and boosted regularized regression. We describe a specialized parallel branch-and-bound method for RMA. 2 - Object-parallel Solution Of Lasso Problems Gyorgy Matyasfalvi, Rutgers University, 100 Rockafeller Road, Piscataway, NJ, 08854, United States, matyasfalvi@gmail.com Jonathan Eckstein We describe an “object-parallel” C++ approach to implementing first-order optimization methods. As an example application, we solve large-scale Lasso problems on a distributed-memory supercomputer with the spectral projected gradient (SPG) method. We can efficiently accommodate highly unbalanced sparsity patterns. 3 - Asynchronous ADMM-like Optimization Algorithms Jonathan Eckstein, Rutgers University, jeckstei@rci.rutgers.edu Drawing on some recent work on asynchronous decomposition methods for monotone inclusions, this talk develops a class of parallel convex optimization algorithms that resembles the alternating direction method of multipliers (ADMM) but operates asynchronously. Unlike prior work on asynchronous variants of the ADMM, the new algorithm’s convergence theory does not rely on either restrictive assumptions on the problem instance or on random invocation of subproblems. Instead, it needs only a basic “fairness” restriction that there be some upper bound on the ratio of the longest and shortest possible subproblem solution times. Stochastic programming applications may also be discussed.

SD20 106C-MCC A Unified Framework for Optimization under Uncertainty

Invited: Tutorial Invited Session

Chair: Warren B Powell, Princeton University, 230 Sherrerd Hall, Dept of Operations Research and Financial Eng, Princeton, NJ, 08544, United States, powell@princeton.edu 1 - A Unified Framework For Optimization under Uncertainty Warren B Powell, Princeton University, 230 Sherrerd Hall, Dept Of Operations Research And Financial Eng, Princeton, NJ, 08544, United States, powell@princeton.edu Stochastic optimization, also known as optimization under uncertainty, is studied by over a dozen communities, often (but not always) with different notational systems and styles, typically motivated by different problem classes (or sometimes different research questions) which often lead to different algorithmic strategies. This resulting “jungle of stochastic optimization” has produced a highly fragmented set of research communities which complicates the sharing of ideas. This tutorial unifies the modeling of a wide range of problems, from dynamic programming to stochastic programming to multiarmed bandit problems to optimal control, in a common mathematical framework that is centered on the search for policies. We then identify two fundamental strategies for finding effective policies, which leads to four fundamental classes of policies which span every field of research in stochastic optimization.

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