Closing the loop in medical decision support by understanding clinical decision-making: A case study on organ transplantation

1 Introduction . The decision that a patient and a clinician jointly make when a donor organ is offered for transplantation is critical and carries serious consequences . Even though the initial offer of a donor organ for a particular recipient is made on the basis of agreed criteria as to optimum allocation ∗Equal contribution 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . within each transplant jurisdiction , there remains substantial variation in the rate at which organs are accepted . Clinical variation is a well-observed phenomenon , but may have profound consequences and unfavourably impact outcomes . Addressing clinical variation is therefore a major priority in many healthcare systems . Understanding the factors which are associated with variation in clinical decision-making is an important goal as it might be able to inform clinicians of biases which , if rectified , might result in improved clinical outcomes . With a case study on organ transplantation , we explore the potential of inverse decision-making approaches to shed light on such factors . Organ transplantation Transplantation is typically the last life-saving treatment available for patients with end-stage diseases that cause organ failures . However , due to the limited availability of donors , patients often have to wait years before transplantation [ 14 , 23 ] . Regrettably , waitlists continue to grow despite increases in the number of donors and many patients die while waiting for an organ , with over 7,500 deaths each year in the United States alone [ 20 ] . The majority of these patients received at least one organ offer that was declined on their behalf [ 12 ] , despite these organs often appearing to be suitable for transplantation and yielding good outcomes when eventually transplanted [ 17 ] . It is therefore important to understand why donor organs subsequently successfully implanted have been declined for previous patients . Clinicians ’ decision-making is poorly understood When a donor organ becomes available , it is first offered to a patient on the waitlist on the basis of agreed offering criteria . Once an organ is offered , a clinician must choose whether to accept or decline the organ offer . Although significant effort has been placed into developing organ allocation algorithms [ 47 , 28 , 4 ] , a donor offered to the first ranked patient in a waitlist is rejected up to 50 % of the time [ 12 , 43 ] . In real-world organ transplantation systems , the performance of organ allocation algorithms are significantly affected by clinicians ’ assessments of organ offers . Even for good quality organs , the assigned organ offers could be turned down several times before they are finally accepted [ 45 , 17 ] . The high ratio of declined organ offers is important as it may impact outcomes for that organ ( e.g . due to prolonged cold ischemia time ) and the patients involved ( e.g . [ 12 ] shows that centers with higher acceptance rates experienced significantly lower adjusted estimated waitlist mortality of the highest-ranked patients ) . Substantial variation in clinical practice Variation in clinical practice is an extensively studied phenomenon across medicine with significant impacts on organ transplantation [ 42 , 2 ] . Striking discrepancies in organ offer acceptance rates have been observed for different transplant centers , even after accounting for organ quality and the severity of the recipient ’ s illness [ 12 ] . However , it has been impossible to disambiguate the causes of this variation , despite its importance for understanding current medical practices and ultimately improving organ allocation policy . In this paper , we highlight that to address such challenges it is necessary for practical inverse decisionmaking approaches to provide interpretable and personalized insights into clinical decision-making . Human interpretable policies Interpretability and transparency are crucial for machine learning applications in medicine [ 39 ] . As a result , white-box models , such as logistic regression , are widely adopted in the medical literature ( e.g . [ 33 , 10 ] ) . Numerous studies have demonstrated improved performance from using black-box models for medical applications [ 15 ] , albeit at the cost of interpretability . The development of interpretable , yet highly performant , models for clinical decision-making is essential to bridge the gap to black-box models and further medical knowledge . In addition , for such interpretations to be useful for understanding clinical decision-making , models must be counterfactually consistent ( i.e . the counterfactual prediction must match the interpretation ) . Precision medicine Precision , or personalized , medicine seeks to improve medical care by tailoring therapy to the needs of a particular patient [ 40 ] . However , in the organ transplantation setting , most existing methods only learn a global decision-making policy for all patients , which fails to address the discrepancies in clinicians ’ policies for patients from different cohorts ( see , e.g. , [ 16 , 5 , 35 ] ) . To both understand clinicians ’ decision-making and improve precision medicine , models that learn personalized policies are necessary . Our contributions In this paper , we propose iTransplant ( individualized TRANSparent Policy Learning for orgAN Transplantation ) , a novel data-driven framework to learn interpretable organ offer acceptance policies directly from clinical data . Our method learns a patient-wise parametrization of the expert clinician policy that accounts for the differences between patients , a crucial but often overlooked factor in organ transplantation . We achieve this by training a neural network-based policy selector to identify individualized policies for patients from different cohorts . These policies act on the space of known match criteria using a white-box function , ensuring interpretability with respect to the match criteria . Our method significantly outperforms existing interpretable models , with comparable accuracy to black-box approaches . We conduct several investigative experiments with real-world liver transplantation data from the Organ Procurement and Transplantation Network ( OPTN ) , covering 190,525 organ offers . The results show that iTransplant can be used to probe clinical decision-making practices in a number of ways . Our investigations allow us to : ( 1 ) identify important match criteria for organ offer acceptance ; ( 2 ) discover patient-wise organ preferences of clinicians via automatic patient stratification in a latent representation space ; and ( 3 ) examine the transplantation practice variations across transplant centers . 2 Problem Formulation . Notation We denote the feature space of all possible patients as X ⊆ Rd and the feature space of all possible organs available as O ⊆ Re . The organ offer that assigns organ O ∈ O to patient X ∈ X is denoted with s = ( X , O ) and the associated decision of clinicians is denoted as a ∈ { 0 , 1 } . Here , the event of offer acceptance is denoted as { a = 1 } , and { a = 0 } means offer rejection . The decision-making policy of clinicians ( expert policy ) is assumed to be a probability distribution π∗ ( a|s ) conditioned on the organ offer s. Following the discussion on interpretability and precision medicine in Section 1 , we propose three key desiderata of practical inverse decision-making approaches : 1 ) personalized policies , 2 ) interpretable insights of decisions , and 3 ) consistent interpretations under perturbation . First , let us introduce some concepts and assumptions related to the above requirements . Criteria space Suppose there are l known match criteria related to decisions on organ offers , denoted by a set of functions C = { ci ( s ) : X ×O → R , i = 1 , 2 , . . . , l } . Each criterion ci ∈ C takes patient and organ features as input and generates a match score as the assessment of organ offer s = ( X , O ) . Based on l match criteria in C , we define a transform T : X × O → M ⊆ Rl that maps organ offers to a criteria space M. Given an organ offer s , its representation in space M can be calculated as T ( s ) : = [ m1 ( s ) , m2 ( s ) , . . . , ml ( s ) ] ′ . In practice , the criteria space would usually be proposed by experts based on domain knowledge . Note , our method allows clinicians to explore criteria space M with different sets of criteria based on their own expertise . In line with our desideratum for a personalized policy , we have Assumption 1 that bridges the criteria space M with the decision-making policy of clinicians on a per patient basis . Assumption 1 ( Partial monotonicity ) There exists a vector v ∈ { −1 , 1 } l such that for any two organ offers s1 = ( X1 , O1 ) , s2 = ( X2 , O2 ) satisfying the conditions of X1 = X2 and v ◦ T ( s1 ) ⪰ v ◦ T ( s2 ) , where ◦ is the Hadamard product operator , we have π∗ ( a = 1|s1 ) ≥ π∗ ( a = 1|s2 ) , where π∗ is clinicians ’ decision-making policy on organ offers . Assumption 2 ( Greedy decision policy ) Noting that decisions on organ offers by clinicians will not necessarily affect the next organ offer assigned to their patients , we further assume that decisions of clinicians are consistent with a greedy policy , i.e. , maximizing the immediate benefit R ( s , a ) of decision a on organ offer s with policy π∗ ( a|s ) = argmaxa∈ { 0,1 } R ( s , a ) . Related to the requirement for a personalized policy , clinicians may use different subsets of criteria in M to evaluate potential outcomes of the offered organ for different cohorts of patients . For instance , [ 5 ] reports that the difference in age of donor and recipient has diverse impacts on post-transplant mortality , and young recipients with elderly donors are most affected . Hence , a personalized reward structure is necessary to account for policy variations at the patient level , which leads to the following assumption : Assumption 3 ( Personalized rewards ) Similar to existing literature on inverse decision-making ( e.g. , [ 49 , 30 ] ) , we assume a linear structure of the reward function : R ( s , a ) = ⟨ρ∗a ( X ) , T ( s ) ⟩ , where ⟨· , ·⟩ is the inner product of two vectors in an Euclidean space , ρ∗a ( X ) is the weight vector for different criteria in space M. Note that there exists a family of equivalence reward functions R ( s , a ) = R ( s , a ) − Φ ( s ) , where Φ ( s ) : X × O 7→ R can be arbitrary scalar functions of s ( see , e.g. , [ 29 ] ) , that are consistent with expert policy π∗ . For the sake of convenience , we assume R ( s , a = 0 ) ≡ 0 and R ( s , a = 1 ) = ⟨ρ∗ ( X ) , T ( s ) ⟩ . It is worth noting that ρ∗ ( X ) is a function of patient features X and thus is able to represent the patient-specific rewards for clinicians . To ensure that the decision policy is differentiable , we further adopt the maximum entropy assumption [ 49 ] that π∗ follows a Boltzmann distribution : π∗ ( a|s ) ∝ exp [ R ( s , a ) ] , which is a soft version of the argmax operator . Note that expert policy π∗ is uniquely determined by the true reward parameter map ρ∗ ( X ) , in this paper , we seek to learn a representation π̂ of expert policy π∗ with all three desiderata achieved by leveraging the notion of a transparent policy space introduced as follows . Transparent policy space Following the Boltzmann distribution formulation of expert policy π∗ , we can construct a transparent policy space Π as Π = { Bernoulli ( p ) : p = 1 1 + exp [ −⟨ρ , T ( s ) ⟩ ] , ρ ∈ Rl } , ( 1 ) where T ( s ) ∈ M. We call the vector ρ ∈ Rl a policy signature since it uniquely characterizes the behavior of the corresponding policy πρ in space Π . Due to the interpretability of criteria space M and the linear structure in equation ( 1 ) , policies in space Π are human comprehensible by design and are considered transparent . Note that for the same patient feature vector X ∈ X , there exists a policy signature ρX = ρ∗ ( X ) such that π∗ ( a|X , O ) = πρX ∈ Π , ∀O ∈ O . Thereby , the expert policy π∗ can be represented with patient-wise projections in space Π. Personalized policy projection Given demonstrations from expert policy π∗ and criteria space M , our target is to find a patient-wise projection π̂ ( θ ) of policy π∗ in the transparent policy space Π such that the distance d ( π∗ , π̂ ( θ ) ) is minimized via minθ d ( π∗ , π̂ ( θ ) ) , where π̂ ( θ ) = πρ ( X ; θ ) and ρ ( X ; θ ) is a function of patient feature vector X with learnable parameter set θ . The distance d ( π∗ , π̂ ( θ ) ) is defined as the accumulated Kullback–Leibler ( KL ) divergence d ( π∗ , π̂ ( θ ) ) : = Es∼△ ( X×O ) [ DKL ( π∗∥π̂ ( θ ) ) |s ] , where △ ( X ×O ) is the organ offer distribution . The requirement for consistency of π̂ ( θ ) is addressed in Section 3 .

Summary: This paper proposes a ML procedure (or framework) for understanding donor organ acceptance decisions for patients on an organ waiting list. Their framework uses NNs for both learning a latent space (encoder/decoder), and for identifying policy criteria for the patient decision function. They include a variety of constraints (consistency, partial monotonicity, model complexity) that are salient to a real organ allocation setting. They lightly discuss their results on real data from UNOS.