2014 — 2015 |
Nohadani, Omid Mehrotra, Sanjay (co-PI) [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Health Systems Optimization Workshop; Chicago, Illinois; 12-13 September 2014 @ Northwestern University At Chicago
The goal of this workshop is to bring together experts from the fields of medicine and operations research in order to identify and address important issues pertinent to improving the cost, quality, efficiency, and efficacy of health systems. Events where experts in both fields can interact are scarce, and this workshop is unique in its expected ability to initiate and foster research collaborations among leading researchers in the two fields that can, through the synergy of their respective expertise, better address the next generation of health systems challenges.
A large number of problems in medicine and health systems can benefit from insights and capabilities developed in the field of mathematical modeling and optimization. At the same time, the difficulty of many of these problems is expected to require new methodological contributions in the field of operations research: accommodating data and model uncertainties, the large-scale nature of real-world problems, the dynamics of changing human anatomy, interplay of systems across scales, and competing policies and criteria, etc. Therefore, this workshop has the potential to advance knowledge in both fields. In addition to allowing key participants from both fields to participate, this award will also allow a large number of graduate students, postdoctoral fellows, and medical interns to take part in the workshop as well.
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0.949 |
2015 — 2018 |
Mittal, Bharat Nohadani, Omid |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Robust Multi-Criteria Optimization With Application to Radiation Therapy @ Northwestern University At Chicago
The research goal of this award is to significantly expand the range of applicability of multi-criteria optimization when input data are uncertain and may exhibit correlations. This includes the collection and analysis of potentially erroneous data, the research of robustness in correlated multi-expert multi-criteria problems, and the validation of the methods on available approaches and probing of results on real data. Many real-world applications put a decision maker in the position to simultaneously achieve multiple contradicting goals. The most common approach is to minimize an aggregate of the objectives, each of which are given a positive weight by its importance. Often, these weights are determined by one or more experts, leading to a sizable level of uncertainty. The results from this research will advance the incorporation of experts' preferences into decision- making and the reliability and reproducibility of decisions. The generality of the approach makes it broadly relevant to real-world problems, many with objectives that are naturally correlated. In radiation therapy, the approach enables oncologists' choices to directly inform the method. Human bias in clinical decisions will be minimized, making optimal cancer treatment accessible, relevant, and ultimately beneficial to patients regardless of medical facility or geographic region. From an educational standpoint, the direct interaction between engineering students and clinicians will offer a unique training ground for the next generation of cross-disciplinary researchers.
Currently, the two main paths in multi-criteria optimization in the presence of errors are Pareto frontier approaches that do not produce one final decision and distributionally robust approaches that are limited to the availability of the distribution. This project focuses on an extension of the latter and is motivated by the application in radiation therapy optimization, as used in nearly two thirds of all cancer cases. This inverse problem typically follows a trial-and-error procedure. The research agenda provides deeper insights on a number of levels into fundamental questions in robust multi-criteria decision-making problems, as well as to establish tools and solutions directly relevant to clinical practice. This project, if successful, can lead to a drastic reduction of both decision time and ambiguity of the outcome, warranting high-quality cancer radiation treatments not limited by human and institutional uncertainties.
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0.949 |