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The development and evaluation of a fuzzy logic expert system for renal transplantation assignment: Is this a useful tool?

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Abstract

Allocating donor kidneys to patients is a complex, multicriteria decision-making problem which involves not only medical, but also ethical and political issues. In this paper, a fuzzy logic expert system approach was proposed as an innovative way to deal with the vagueness and complexity faced by medical doctors in kidney allocation decision making. A pilot fuzzy logic expert system for kidney allocation was developed and evaluated in comparison with two existing allocation algorithms: a priority sorting system used by multiple organ retrieval and exchange (MORE) in Canada and a point scoring systems used by united network for organ sharing (UNOS) in US. Our simulated experiment based on real data indicated that the fuzzy logic system can represent the expert's thinking well in handling complex tradeoffs, and overall, the fuzzy logic derived recommendations were more acceptable to the expert than those from the MORE and UNOS algorithms.

Introduction

Renal transplantation is recognized as being of significant benefit to patients in renal failure, and the demand for donor allografts continues to rise. The allocation of scarce health-care resources has been the subject of much debate (Brich and Gafni, 1992; Lawrence et al., 1993). As noted by Childress (1989), common law in the USA (and elsewhere) has recognized organs as “quasi-property” and in most jurisdictions public and social policies have been invoked to resolve the issues surrounding disbursement of solid organs for transplantation. Underlying this debate has been the implicit and explicit understanding that respect for persons, utility (to maximize good) and justice (equity) are the principles underlying any allocation system. These principles can be undermined by bias occurring prior to as well as subsequent to recipients being accepted onto the transplant waiting list (Alexander and Sehgal, 1998). The debate over the “rules of allocation” has been protracted and frequently fractious (McNamee, 1996; Starzl and Fung, 1996; Briggs, 1996; Chang, 1996). Indeed, as noted by Terasaki et al. (1996), the term “allocation” is probably a misnomer, as distribution of kidneys for transplantation more closely resembles a “lottery”, albeit one with winnable odds. Some authorities have emphasized equity as the major criterion for allocation, while others have argued that optimal outcome of the renal allograft should be a critical factor in allocation, as graft failure simply increases the pressure on this scarce resource by returning recipients to the waiting pool. It is important to investigate the possible balances between these two criteria (Yuan et al., 1994).

In the absence of any agreement about the best balance between “equity” and “medical utility” in any given individual renal allocation situation, most organ procurement organizations (OPOs) have opted for a rigid hierarchical set of rules to distribute cadaver donor kidneys, with physicians being given the right to over-ride these decisions at the local level, subject to auditing. In general, marginal or less well-defined predictors of improved outcome have been sacrificed in order to allow physicians and OPOs to arrive at mutually acceptable allocation algorithm compromises (Starzl and Fung, 1996; Zachary et al., 1997).

“Experts” in transplantation have been most active in clarifying the issues surrounding “medical utility”. While rigid allocation algorithms have solved some problems, the potential benefits of complex interactions have been correspondingly sacrificed. A decision-making system that could incorporate such complexities but minimize physician or OPO bias at the individual allocation event would be very helpful, particularly if such a system could be tested and evaluated prospectively in a “real-life” simulation.

Vagueness and ambiguity are often involved in human reasoning. A physician often faces a dilemma such as: “Should a cadaver kidney from a middle aged male donor be given to a poorly matched, highly sensitized young female who has been waiting for a long time in preference to a non-sensitized, very well matched older recipient who has been on the waiting list for a moderate period of time?” Individuals can make these choices, but their decisions are subject to bias and non-reproducibility. Existing allocation algorithms can generate a solution but do not directly represent physicians' intuitive thinking. We believe that fuzzy set theory and fuzzy logic introduced by Zadeh (1965) are inherently well suited to solving this particular problem. A fuzzy logic expert system can be used to represent physicians' intuitive thinking well, and at the same time avoid inconsistency and emotional biases.

The objective of our research was to investigate whether or not a fuzzy logic expert system for kidney transplant allocation could perform better in comparison with existing allocation algorithms. What follows are the explanations of kidney allocation criteria and existing allocation algorithms; the introduction and implementation of a fuzzy logic approach for kidney allocation; the simulation and the analysis of testing results; and finally, the conclusion and further research directions.

Section snippets

Kidney transplant allocation criteria

Kidney allocation is a complex, multiple criteria decision-making problem. Here we list several major criteria under consideration. Our focus is to show the nature of these criteria. It is not our intention to make the list complete, since the decisions regarding which criteria should be included are themselves very complex.

Current allocation schemes

It is clear that all the above criteria are stated in natural language and by nature they are fuzzy. The interaction and tradeoffs among them are also not well defined. Many algorithms have been proposed to convert these criteria into a simple, more or less rigid set of guidelines in order to eliminate vagueness. Here we discuss two important allocation algorithms. The first algorithm was used by the multiple organ retrieval and exchange (MORE) program of Ontario in Canada. The second was used

A fuzzy logic approach for kidney allocation

Fuzzy set theory and fuzzy logic were established in 1965 by Zadeh in order to deal with the vagueness and ambiguity associated with human thinking, reasoning, cognition, and perception processes (Zadeh, 1965, Zadeh, 1973; Zimmermann, 1987). Fuzzy logic has been successfully used in solving many decision making and industrial control problems (Hruschka, 1988) and has yielded results superior to those obtained from traditional numerical methods (Munakata and Jani, 1994). It has also been used in

Simulation objectives and hypotheses

One main objective of the simulation was to determine whether or not the fuzzy system could accurately approximate Dr. Expert's thinking. Therefore, it was necessary to check how closely Dr. Expert's choices of suitable patients matched with those of the system. As a form of control group, it would also be interesting to see how his choices compared to those made by both the UNOS and MORE algorithms. However, the interesting aspect of the simulation lay in the fact that Dr. Expert was being

Analysis of testing results

The simulation test results consist of the ranking orders of the possible recipients generated by different methods for each donor kidney against the waiting list. We want to compare the ranking results between two methods, the experts' manual choices versus an algorithm's choices. Three types of measurements will be used to measure the differences: the “overlapping rate”, the “rank consistency index”, and “the first choice breakdown”.

The overlapping rate is the percentage of candidate

Conclusion and further research

We have successfully developed a pilot fuzzy logic expert system for kidney allocation based on a kidney transplant expert's knowledge. The fuzzy logic system can represent the expert's thinking well in handling complex tradeoffs. A simulated kidney allocation experiment based on real donor and patient data has shown that overall, the fuzzy logic derived recommendations were more acceptable to the expert than those from the MORE and UNOS algorithms. However, in order to derive a more general

Acknowledgements

We wish to thank the staff at MORE Ontario for their help with the case data. Thanks also go to anonymous referees who provide constructive comments for the improvement of the paper.

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