A Fielded Testbed for AI & Healthcare
A 3.5-hour tutorial to be held on Friday, February 12 at the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16).
This tutorial covers past and current research in organ exchange, a method by which patients in need of a organ can swap willing but incompatible donors. Throughout, it also gives a higher-level overview of the steps taken to translate a purely academic idea into a large fielded healthcare system.
We focus on the computational aspects of organ exchange, starting by introducing past research that has now been implemented in real-world kidney exchange (or deemed impractical and not yet fielded) and then by covering the current research problems available at the intersection of AI, optimization, and economics. We especially dives into the computational methods developed and used to solve extremely large discrete optimization problems that reflect kidney exchange, along with the interplay between modeling decisions, computational tractability, exchange efficiency, equity, dynamism in matching, and a variety of other real-world constraints and considerations.
While we focus on kidney exchange, research toward exchanges for other organs such as livers and lungs, as well as cross-organ exchanges, will also be covered.
This tutorial will be accessible to the general attendee of AAAI and should not require specialized knowledge in a specific subfield of AI.
John is a final-year Ph.D. candidate in the Computer Science Department at Carnegie Mellon University. He has published extensively on kidney and general organ exchange in AI and OR venues; that work has set policy at the United Network for Organ Sharing (UNOS) nationwide kidney exchange. He is an NDSEG Fellow, Facebook Fellow, and Siebel Scholar.
Tuomas is Professor at CMU’s Computer Science Department. His algorithms run the national kidney exchange of 143 transplant centers; his kidney exchange designs have been adopted worldwide. He published 450 papers and fielded 800 largest-scale combinatorial auctions totaling $60 billion. He is Founder/CEO of Optimized Markets, for advertising campaign sales/scheduling.
The following is an outline for our 3.5-hour tutorial on kidney exchange. It assumes an intermission at the 1.75-hour mark. We include a reference or two to a sample paper related to a specific point in the outline in the pdf version of this outline. These references are by no means meant to be exhaustive!
At this point in the tutorial, the audience will know about the high-level kidney exchange problem, its initial formulation as a theoretically and empirically hard optimization problem, and will have in-depth knowledge of the original techniques and AI/OR methods used to overcome that computational hardness. The presenters have worked extensively with very large kidney exchanges to implement these clearing algorithms in practice. We will cover some of the early challenges to translating AI-style research into practice, such as the following.
We will discuss how these challenges were overcome, with learnings that will hopefully help AI researchers get AI systems fielded in the large in other applications as well.
Following the intermission, we will deep dive into different dimensions of fielded kidney exchange that are most pertinent to today's and the future's fielded exchanges, currently unsolved, and would benefit immensely from AI research.
In all fielded kidney exchanges, many algorithmic matches fail to move to transplant. This is not explicitly taken into account in any fielded optimization algorithms, but is an active area of research.
Prioritization of otherwise-marginalized candidates is a contentious topic and ongoing discussion in healthcare and, specifically, kidney exchange. We will cover the following.
Healthcare applications are often dynamic, with patients and other agents arriving and departing over time, and the interactions between the various agents changing over time as well. In kidney exchange, patients and donors arrive over time, and the exchange can choose when, how, and if to match these actors. We will cover general dynamic optimization techniques developed and then applied to kidney exchange, along with theoretical approaches to dynamic kidney exchange and matching. This is an open problem; we will also cover promising research directions in dynamic exchange, with an emphasis on AI approaches to the problem.
We present a holistic method to combine all of the dimensions mentioned above, along with high-level human-provided value judgments, into a unified framework for learning to match in a general dynamic model. The framework takes as input a high-level objective (e.g., "maximize graft survival of transplants over time") decided on by experts, then automatically (i) learns based on data how to make this objective concrete and (ii) learns the "means" to accomplish this goal—a task, in our experience, that humans handle poorly. It uses data from all live kidney transplants in the US since 1987 to learn the quality of each possible match; it then learns the potentials of elements of the current input graph offline (e.g., potentials of pairs based on features such as donor and patient blood types), translates these to weights, and performs a computationally feasible batch matching that incorporates dynamic, failure-aware considerations through the weights.
This method, shown in Figure 1, provides sensitivity analysis for the UNOS exchange. We will discuss challenges in implementation and the disconnect between general optimization methods and a fielded application, and how the given framework attempts to unify the two different viewpoints.
Figure 1. The FutureMatch framework.
Here, we view the kidney exchange problem as a multi-agent system with transplant centers as agents, and their set of patient-donor pairs (and possibly altrustic donors) as a private type. The issue is that the transplant centers can match easy-to-match pairs locally and only report hard pairs to the exchange because transplant centers gain financially from conducting surgeries locally and local transplants are logistically easier. Such hiding of the pairs from the exchange compromises the global efficiency of the system. Prof. Sandholm in collaboration with UNOS has empirically shown that the problem is rampant: today transplant centers not only hide their easy-to-match pairs but all their internally matchable pairs. We will discuss mechanism design approaches to this problem, such as the following.
We will briefly overview nascent theoretical and practical pushes to field new types of organ exchanges—liver exchange, lung exchange, multi-organ exchange—with a focus on the modeling differences between those and kidney exchange.
We will provide insight into where we think fielded kidney exchanges are—and where they should be—going, and touch on the different directions in this area where AI researchers could make an impact.
[1] | David Abraham, Avrim Blum, and Tuomas Sandholm. Clearing algorithms for barter exchange markets: Enabling nationwide kidney exchanges. In Proceedings of the ACM Conference on Electronic Commerce (EC), pages 295--304, 2007. |
[2] | Mohammad Akbarpour, Shengwu Li, and Shayan Oveis Gharan. Dynamic matching market design. In Proceedings of the ACM Conference on Economics and Computation (EC), page 355, 2014. |
[3] | Ross Anderson, Itai Ashlagi, David Gamarnik, and Yash Kanoria. A dynamic model of barter exchange. In Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2015. |
[4] | Ross Anderson, Itai Ashlagi, David Gamarnik, and Alvin E Roth. Finding long chains in kidney exchange using the traveling salesman problem. Proceedings of the National Academy of Sciences, 112(3):663--668, 2015. |
[5] | Elliot Anshelevich, Meenal Chhabra, Sanmay Das, and Matthew Gerrior. On the social welfare of mechanisms for repeated batch matching. In AAAI Conference on Artificial Intelligence (AAAI), pages 60--66, 2013. |
[6] | Itai Ashlagi, Felix Fischer, Ian A Kash, and Ariel D Procaccia. Mix and match: A strategyproof mechanism for multi-hospital kidney exchange. Games and Economic Behavior, 91:284--296, 2015. |
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