Using deceased-donor kidneys to initiate chains of living donor kidney paired donations: algorithms and experimentation

We design a flexible algorithm that exploits deceased donor kidneys to initiate chains of living donor kidney paired donations, combining deceased and living donor allocation mechanisms to improve the quantity and quality of kidney transplants. The advantages of this approach have been measured using retrospective data on the pool of donor/recipient incompatible and desensitized pairs at the Padua University Hospital, the largest center for living donor kidney transplants in Italy. The experiments show a remarkable improvement on the number of patients with incompatible donor who could be transplanted, a decrease in the number of desensitization procedures, and an increase in the number of UT patients (that is, patients unlikely to be transplanted for immunological reasons) in the waiting list who could receive an organ.Comment: To be published in AIES 201

Voting with Random Classifiers (VORACE)

In many machine learning scenarios, looking for the best classifier that fits a particular dataset can be very costly in terms of time and resources. Moreover, it can require deep knowledge of the specific domain. We propose a new technique which does not require profound expertise in the domain and avoids the commonly used strategy of hyper-parameter tuning and model selection. Our method is an innovative ensemble technique that uses voting rules over a set of randomly-generated classifiers. Given a new input sample, we interpret the output of each classifier as a ranking over the set of possible classes. We then aggregate these output rankings using a voting rule, which treats them as preferences over the classes. We show that our approach obtains good results compared to the state-of-the-art, both providing a theoretical analysis and an empirical evaluation of the approach on several datasets

Deceased donor-initiated Chains: first report of a successful deliberate case and its ethical implications

Background: The utilization of deceased donor kidneys to initiate chains of living donor kidney paired donation (KPD) has been proposed, although the potential gain of this practice needs to be quantified and the ethical implications must be addressed before starting its application. Methods: The gain of implementing deceased donor-initiated chains has been measured through a mathematical algorithm, using retrospective data on the pool of donor/recipient incompatible pairs at a single Center. Allocation rules of chain ending kidneys and characteristics/quality of the chain initiating kidney (CIK) are described. Results: the quantification of benefit analysis showed that with a pool of 69 kidneys from deceased donors and 16 pairs enrolled in the KPD program, over a period of 3 years it is possible to transplant 8/16 recipients (50%). Following the approval of the Bioethical Committee of the Veneto Region and the revision of the allocation policies by the Italian National Transplant Center, the first successful case has been performed. The waiting time of the recipient (male, 53 yo) after entering the program for the CIK with a kidney donor risk index (KDRI) equal to 0.61 and a kidney donor profile index (KDPI) of 3%, was 4 days. His willing donor (female, 53 yo) with a living kidney donor profile index (LKDPI) of 2, donated 2 days later to a chain ending recipient (male, 47 yo,) who had been on dialysis for 5 years. Conclusions: This is the first report of a deliberate deceased donor-initiated chain, which has been successfully performed. This has been made possible thanks to an extensive phase of evaluation of the ethical issues and allocation policy impact. This paper includes a preliminary efficacy assessment and the development a dedicated algorithm

AI Hilbert: A New Paradigm for Scientific Discovery by Unifying Data and Background Knowledge

The discovery of scientific formulae that parsimoniously explain natural phenomena and align with existing background theory is a key goal in science. Historically, scientists have derived natural laws by manipulating equations based on existing knowledge, forming new equations, and verifying them experimentally. In recent years, data-driven scientific discovery has emerged as a viable competitor in settings with large amounts of experimental data. Unfortunately, data-driven methods often fail to discover valid laws when data is noisy or scarce. Accordingly, recent works combine regression and reasoning to eliminate formulae inconsistent with background theory. However, the problem of searching over the space of formulae consistent with background theory to find one that fits the data best is not well-solved. We propose a solution to this problem when all axioms and scientific laws are expressible via polynomial equalities and inequalities and argue that our approach is widely applicable. We further model notions of minimal complexity using binary variables and logical constraints, solve polynomial optimization problems via mixed-integer linear or semidefinite optimization, and prove the validity of our scientific discoveries in a principled manner using Positivestellensatz certificates. Remarkably, the optimization techniques leveraged in this paper allow our approach to run in polynomial time with fully correct background theory, or non-deterministic polynomial (NP) time with partially correct background theory. We demonstrate that some famous scientific laws, including Kepler's Third Law of Planetary Motion, the Hagen-Poiseuille Equation, and the Radiated Gravitational Wave Power equation, can be derived in a principled manner from background axioms and experimental data.Comment: Slightly revised from version 1, in particular polished the figure