Closing the loop in medical decision support by understanding clinical decision-making: A case study on organ transplantation
Authors: Yuchao Qin
Fergus Imrie
Alihan Hüyük
Daniel Jarrett
Alexander Edward Gimson
Mihaela van der Schaar
Venue: NeurIPS 2021 Poster (2021)
Keywords: Healthcare
Understanding human decisions
Inverse decision-making
Augmenting clinical decision support

Abstract: Significant effort has been placed on developing decision support tools to improve patient care. However, drivers of real-world clinical decisions in complex medical scenarios are not yet well-understood, resulting in substantial gaps between these tools and practical applications. In light of this, we highlight that more attention on understanding clinical decision-making is required both to elucidate current clinical practices and to enable effective human-machine interactions. This is imperative in high-stakes scenarios with scarce available resources. Using organ transplantation as a case study, we formalize the desiderata of methods for understanding clinical decision-making. We show that most existing machine learning methods are insufficient to meet these requirements and propose iTransplant, a novel data-driven framework to learn the factors affecting decisions on organ offers in an instance-wise fashion directly from clinical data, as a possible solution. Through experiments on real-world liver transplantation data from OPTN, we demonstrate the use of iTransplant to: (1) discover which criteria are most important to clinicians for organ offer acceptance; (2) identify patient-specific organ preferences of clinicians allowing automatic patient stratification;  and (3) explore variations in transplantation practices between different transplant centers. Finally, we emphasize that the insights gained by iTransplant can be used to inform the development of future decision support tools.

Closing the loop in medical decision support by understanding clinical decision-making: A case study on organ transplantation
Yuchao Qin University of Cambridge yq2570cam. aw..cammm......aac.mmmmmmmm uk
Fergus Imrie
Alihan Hüyük
University of California, Los Angeles
University of Cambridge
imrie0g. ucla.
ah20750cam ac uk
Daniel Jarrett
Alexander Edward Gimson
University of Cambridge
University of Cambridge
daniel. jarrett@maths cam uk
al alexander gimsonenhs net
Mihaela der Schaar
University of Cambridge
The Alan Turing Institute University of California, Los Angeles mv4720cam. ac uk
Abstract
Significant effort has been placed on developing decision support tools improve patient care. However, drivers of real-world clinical decisions in complex medical scenarios are not yet well-understood, resulting in substantial gaps between these tools and practical applications. In light of this, we highlight that more attention on understanding clinical secision-making practices and to enable effective human- machine interactions. This is imperative high-stakes scenarios with scarce available resources. Using organ transplan- tation as case study, we formalize the desiderata of methods for understanding We show that most existing machine learning methods are insufficient to meet these requirements and propose i'ransplant, novel data- driven framework to learn the factors affecting decisions on organ offers an instance-wist fashion directly from clinical data, possible solution. Through experiments on real-world liver transplantation data from OPTN, we demonstrate the use of i'Transplant to: (1) discover which criteria are most important to clini- cians for organ offer acceptance; (2) identify patient-speciff organ preferences clinicians allowing automatic patient stratification; and (3) explore variations in transplantation practices between different transplant centers. Finally, we empha- size that the insights gained by iTTransplatt be used to inform the development future decision support tools.
required both to elucidate current clinical
clinical decision-makin
Introduction
The decision that patient and clinician jointly make when donor organ is offered for trans- plantation is critical and carries serious consequences. Even though the initial offer of a donor organ for particular recipient is made the basis of agreed criteria to optimum allocation
Equal contribution
35th Conference on Neural Information Processing Systems (NcurIPS 2021).

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Decision support tools
suppect segeetlons,...
Knowledge
Automatic feedbacko
dleisions
Understondings buman decisions
ATransplnt
Domain experts, machine Jearning engineers,...
Figure 1: Closing the loop of medical decision support by understanding clinical decision-making Most existing clinical decision support tools offer generic advice or alerts to clinicians without specificity for the immediate decision about to be taken. In this paper, highlight that iTransslant, by identifying drivers that decision, will be able to feedback into future iterations of the decision support tool specific information about which factors impacted the decisions that in future that specific information can be given to the decision maker or can be taken into account in designing future iterations of the decision support tool itself.
within each transplant jurisdiction, there remains substantial variation in the rate at which organs are accepted. Clinical variation is well-observed phenomenon, but may have profound consequences and unfavourably impact outcomes. Addressing clinical variation therefore a major priority in many healthcare systems. Understanding the factors which associated with variation in clinical decision-makee is an important goal as might be able to inform clinicians of biases which, rectified, might result in improved clinical outcomes. With case study organ transplantation, explore the potential of inverse decision- 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 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]. therefore important to understand why donor organs subsequently successfully implanted have been declined for previous patients.
Clinicians' decision-making is poorly understood When donor organ becomes available, is first offered to 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], donor offered to the first ranked patient waitlist is rejected up to 50% 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, 17J. 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 organ offer acceptance rates have been observed for different transplant centers. even after accounting for organ quality and the severity of the recipient's 's illness [12]. However, it has been impossible to disambiguate the causes this variation, despite its importance for understanding current medical practices and ultimately improving organ allocation policy.
In this paper, highlight that to address such challenges it is necessary for practical inverse decision- making approaches to provide interpretable and personalized insights into clinical decision- makinn...
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

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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 makiin policy for all patients, which fails address the discrepancies in clinicians' policies for patients from different cohorts (see, e.g., [16, 5, 35]). To both understand clinicians' Dpeision-making and improve precision medicine, models that learn personalized policies are necessary.
Our contributions In this paper, 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-wiss parametrization of the expert clinician policy that accounts for the differences between patients, crucial but often overlooked factor in organ transplantation.
We achieve this by training neural network-baseee policy selector to identify individualized policies for patients from different cohorts. These policies act 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-we wise organ preferences of clinicians via automatic patient stratification in 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' S Red and the feature space of all possible organs available as O C R The organ offer that assigns organ O E O to patient X E & is denoted with s = (X, O) and the associated decision of clinicians is denoted as E {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 n² (a)s) conditioned on the organ offer s.
Following the discussion on interpretability and precision medicine in Section I, we propose three key desiderata 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 the above requirements.
Criteria space Suppose there are known match criteria related to decisions on organ offers, denoted by a set of functions C = (s)( X x O - R, i = 1,2,...., .). Each criterion c; C takes patient and organ features as input and generates a match score as the assessment of organ offer (X, O). Based on match criteria in C, we define a transform T: X' x O M C R 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)= [m] (s), mm(s))).......)..)..,).,)))))m (s) m{())] 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 that bridges the criteria space M with the decision makiing policy of clinicians a per patient basis.
Assumption 1 (Partial monotonicity) There exists a vector v E {-1, such that for any two organ offers ve T(22), where o the Hadamard product operator, we have nT" (a- = 1/s1) 2 x xa(a = 1 1(82), where nt is clinicians' decision decision-making making policy organ offers.
(X1, Or), 82 (X2, O2) satisfying the conditions of X, X2 and v T(s)) >
3

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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 greedy policy, i.e., maximizing the immediate benefit R(ss a) of decision a on organ offer s with policy " =a s) = arg (0,1) R(s,a). a).
Related to the requirement for a personalized policy, clinicians may use different subsets of criteria in M 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 linear structure of the reward function: R(s, a) (%% (X), T())., where ( is the inner product of two vectors in an Euclidean space, (X) is the weight vector for different criteria in space M. Note that there exists a family of equivalence reward functions R(s, a) = A(s. a) (S)), where (s)) X x O + I can be arbitrary scalar functions of s (see, e.g., [. that are consistent with expert policy π². For the sake of convenience, we assume R(s, a = 0) = O and R(s a 1) (o" (X), (s)). It is worth noting that (xx) is a function of patient features and thus is able to represent the patient- sprrint--pecific rewards for clinicians.
To ensure that the decision policy is differentiable, we further adopt the maximum entropy assumption [49] that x' follows a Boltzmann distribution: x² (als) exp (R(s, a)}, which is sefi version of the arg max operator. Note that expert policy x¹ is uniquely determined by the true reward parameter map (X), in this paper, we seek to learn representation मे of expert policy n² 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 x². we can construct a transparent policy space II as
1
II (Bernoullip)): p 1 exp (- [())) P E R'),
(1)
where T(s) E .M. We call the vector & E IR' policy signature since it uniquely characterizes the behavior of the corresponding policy яp in space 11. Due to the interpretability of criteria space M and the linear structure in equation (1), policies in space Il are human comprehensible by design and are considered transparent. Note that for the same patient feature vector X E X, there exists a policy signature px= p (X) such that x x'(aa X, O) = Txx E II. AA E O. Thereby, the expert policy x² can be represented with patient-wist wise projections in space II.
Personalized policy projection Given demonstrations from expert policy n¹ and criteria space M, our target is to find a patient-wise projection #10) of policy n" in the transparent policy space Il such that the distance d(=*, (()))
(00)) is minimized via ming d(TT",(())) #(1),, where #(0) Tx(X:0) and p(X; f) is a function of patient feature vector X with learnable parameter set 0. The dis- tance d(="', (0)) is defined as the accumulated Kullback-Leible (KL) divergence ((T",=(0) = Eg~ [DxL (TT [| [s] where 4 x O) is the organ offer distribution. The requirement for consistency of # (0) is addressed in Section 3.
3 Individualized Transparent Policy Learning
In this paper, we propose data-driven framework, i'Transplant, for learning patient-wise policies that match clinical practice. We utilize neural networks as general function approximators for the policy signature p(X; 0). The architecture of the proposed model is illustrated in Figure 2.
While the idea in [22] of mapping the input features into concept space and performing prediction tasks using the concept space is in philosophy similar to our proposed method, the target of our method differs significantly from [22]. [22] aims to predict a set of numan--peciffe intermediate step, with each concept having a fixed (albeit learned) importance on the final prediction. Contrastingly, iTTransplatt aims to predict individualized policies (equivalently a set of weights) over a set of dlinicinnsspccfiee criteria, but not the fixed values of such criteria. The distinct targets lead to significant differences in the problem formulation, methodology and analysis in our paper and in [22].
concepts as an

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Network Structure of Friannspaant
Fi Godenn
Aecendrrcction
Consistency
Transparent Policy Space II
Reconsinicted PaticndFeature
Decoder
Mak Layer
Query
Compass
Patieti
Repreaentation
Policy Signattee
Individhalioodd Polley
Encoder
Policy Selector
Policy
Statification
Individualined Palicy Mapping
Figure 2: Network structure the iTTansslant framework.
In the proposed iTransplant framework, organ offers s = (X, O) are mapped to criteria space M via transform T specified by domain experts. The transparent policies identified by iTransplant all generated based the match criteria in space .M. To achieve individualized policy identification, an auto-encoder structure is utilized to learn the latent representation Z 2 of patient features X. From Z, a specific policy signature is generated by the policy selector network. Finally, the individualized policy <a(a)s) is retrieved from the transparent policy space IT via the policy signature This design ensures that the policies learned by iTransplant are innately individualized and human comprehensible. Detailed descriptions of the network structure and loss functions can be found in the Appendix.
Policy guided patient stratification As shown in Figure 2, the policy selector network is built on top of a Mixture-of of-extssss (MoE) layer [36]. which contains a gating network and K expert networks. Based the latent representation Z of patient, the gating network will select experts among the total candidates and combine their outputs as the policy signature p. The MoE structure is applied encourage the encoder network group patients sharing the same decision policy in the latent space (see the Appendix for illustrations). The MoE layer in the policy selector network passes the gradient from the policy projection loss Lotlicy back the encoder network, enabling to guide the encoder to map patients share the same policy as neighbours in the latent space Z. In this sense, in iTransplant, the representation learning of patient features in the latent space is guided by the learned policy, which allows the encoder network to learn the implicit stratification of patients from real-wor world transplantation data.
Individualized policy mapping Our main target is to find the patient-wise policy projec- tion (1)) TT((:0) II expert policy x such that the distance d(=", a1(0) is min- imized. Note that the entropy of expert policy '' is constant, we have a(TT,=(0)) (0)=A(xx)) ="(ass log( (ag(=))))))) Constant, where the first term is the accumulated cross-entropy between nt and a (9), Given demonstrations D {(s,, a;): 1, . policy π², the minimization of such accumulated KL-divergence can be achieved by minimizing the following policy projection loss LPaticy
.N } of expert
ΣNn Ene (0.1) I(a, a) log (0) (a: (s:)).
Enforcing the consistency requirement Note that policies in space 11 are innately consistent under perturbations to organ features. To meet the consistency requirement for inverse decision-making methods proposed in Section 2, regularization term defined in equation (2) is enforced to improve consistency policy projection II for patients with similar latent space representations.
| P2 22
CConsistency
x1 X2
(2)
|||
where A(X' X)) the patient pair distribution over space X' P1. P2 and Z Z2 are policy signatures and latent representations for patients and X2, respectively, À > 0 is hyperparameter.
4 Related Work
Medical literature on organ offer acceptance A number of works have attempted discover variables related to organ offer acceptance or rejection [27, 46] and to improve these decisions [41].
5

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For instance, [33] used logistic regression models to identify donor features related to a higher risk of liver allograft discard and proposed a discard risk index that could be used to improve existing organ allocation policies. This donor discard index describes donor factors but does not address the reasons why specific center may or may not reject a donor organ.
Comparison with Related Work. iTransplant satisfies three key desiderata for understanding clinicians' decision-makingg for organ transplant offers: (1) individualized policies; (2) interpretability;; (3) consistency of interpretation. Organ offers, decisions of clinicians, patient feature vector, criteria space for policy generation, expert policy, recovered policy, feature selection function and learned contributions of different criteria to decisions are denoted as S, M, T²", म., S and ý. respectively.
Table 1:
APPROACH
EXAMPLE REF
INPUT
OUTPUT
(1)
(2)
(3)
IMITATION LEARNING
[38]
(st. ae) ~ x²
4
X
X
X
GLOBAL
[31]
(s) ax) ~==
R
X
X
X
VARIABLE SELECTION
INSTANCE-WISS
[48]
{st: az) ~m
#, St)(
X
VARIABLE SEL DELECTION
INVERSE REINFORCEMENT
[49]
{sr, az) ~ ~M
X
V
X
LEARNING'
ITRANSPLANT
(OurS)
{s,, az) n:' MM
(X)(
/
In this paper, we also seek to identify key variables and criteria that inform clinical decisions on organ offers. Therefore, our method is primarily designed as a tool for understanding the different factors or the weight of those factors between decision-making of clinicians rather than a model to describe commonly rejected donors. Our method learns clinicians' policies at the patient level rather than global policy, enabling our method to identify different policies for different patient cohorts.
Because the information derived from iTrransplatt is specific to donor offered center, it may act as a hetter prompt to de-bias clinical decision-making. Prior attempts to de-bias clinical decision- making have involved generic prompts relating to common errors in decision-making [1, 7, 8] and have been met with variable success. In contrast, our approach identifies specific factors that are involved in individual donor organ - patient acceptance decisions.
Both global [13, 31] and instance-wise [48] feature selection methods could be applied to identify important variables involved in organ offer decisions. In comparison, iTransplant is, by design, capable of discovering important variables for decisions on organ offers at both the global and patient level. In addition, unlike methods which represent policies directly by neural networks, our method offers interpretability via the transparent policy space II, which is critical for high-stake scenarios such decision-makee makinn for organ offers.
Interpretation by feature-selection
Imitation learning with post-hoc interpretation Imitation learning (II.) [18] seeks to replicate scenario considered in this paper, behavioral cloning (BC) [3, 38) could be directly applied to imitate the expert policy. However, as a black-box model, the neural networks utilized in BC provide no directly human comprehensible interpretations to the learned policy and have to rely on post-hoc interpretability methods like LIME [34] and SHAP [26] provide insight for their predictions.
expert polices from observational data of decision-making In the one-step decision-
Inverse reinforcement learning Compared imitation learning, inverse reinforcement learning (IRL) techniques [32, 30] seek to infer a reward function that is consistent with observed expert behaviors. The learned reward function can then be used for explanation of expert behaviors. For instance, with feature maps from domain knowledge and the assumption of linear reward structure, maximum entropy IRL [49] can effectively reveal contributions of different features to expert behaviors. Note that in the one-step decision-making setting considered in this paper, maximum entropy IRL [49] degenerates to a global logistic regression model, which means that it unable to capture the variations in decisions made for different subgroups of patients.
In the setting considered in this paper, IRL methods with assumptions of maximum eextmmum-entropy entropy policy and linear reward structures degenerate to logistic regression. In this case, their interpretations become consistent under perturbations. However, many IRL methods would be inconsistent in general settings.

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A comparison of iTrnnspaant with alternate methods that could be applied to discover potential drivers of clinicians' decisions in the organ transplantation setting provided in Table 1.
Illustrative Examples
To demonstrate the advantage of the proposed individualized policy learning framework and showcase potential applications of iTransplant as a tool for discovering variations transplantation practices, real-world liver transplantation data from OPTN are utilized to provide several illustrative examples. In all examples included in this section, the criteria space M is constructed from a set of match criteria manually specified based on domain knowledge, and detailed explanation of these match criteria can be found in the Appendix, together with full experimental details.
5.1 Model Validation
We first sought to validate the ability of i'ransplant to predict organ offer acceptance using organ offer data from transplant centers with sufficient number (over 1,000) of accepted offers after removal of missing data. We compared i'Transplnnt to a variety of baselines encompassing the approaches outlined in Table 1, including logistic regression, white-box model frequently used in the medical literature, and neural network-basee behavioral cloning (BC). Despite being a black-box model, and thus unsuitable achieve our goal of understanding decision-making we include BC as upper bound on predictive performance using the proposed match criteria. We measure performance using three metries: area under the receiver operating characteristic precision recall curve (AUC-PRC) and log-likelihood (LL) of observed samples. Hyperparameters for all methods can be found in the Appendix.
(AUC-ROC), area under the
Table 2: Benchmark of different methods on decision prediction.
METHOD
AUC-ROC
AUC-PRC
LL
LOGISTIC REGRESSION
0.79420.055
0.341
-0.53820.051
PER-CLUSTER LOGISTIC REGRESSION
0.803+0.043 0.49
0.352+0.06 063
-0.52720 .048
DECISION TREE
0.775+0.057
0.274+0.069
-0.552+0.06 060
PER-CLUSTER DECISION TREE
0.773+0.055
0.281
-0.564+0.06
LOCALLY WEIGHTEd REGRASSION
0.865=0.04
0.429=0.066
-0.256#0.00 089
LASSO
0.777
0.31420.068
-0.570=0.045
RANDOM FOREST
0.85240.06 0.64
0.42140.100
-0.271
INVASE
0.790=0.06 .062
0.341+0.071
-0.541
BEHAVIORAL CLONING
0.899=0.04 043
0.502+0.067
-0.383+0.06
ITRANSPLANT (OurS)
0.895+0.045
0.50240.062
-0.39620.061
As shown in Table 2, iTransplant significantly outperforms all baseline methods and performs similarly to the upper bound provided by BC while maintaining interpretability of policies identified for each patient. Although the policy learned by i'Trnnspant uses logistic regression as the decision function, by learning individualized policies, i'Transplant greatly outperformed both global logistic regression and per-cluster logistic regression (see Appendix for further details).
While BC and INVASE are unable provide direct/sufficient insight into clinical decision-making (our primary goal), post-hoc interpretations could offer an alternate approach. To assess the suit- ability of such interpretations, we evaluated the consistency of the interpretations of iTrrnsplaatt and BC/INVASE with post-hoc interpretation via LIME [34]. If a feature has a positive weight, increasing the value of the feature should increase the probability of acceptance. Indeed, this is requirement for the interpretation to be useful. Thus we performed perturbations to single patient or organ features (25% of standard deviation for con- tinuous variables and flipped value for binary ones) and measured the consistency sign between the actual devi- ation in prediction and the expected deviation from coun- terfactual interpretation (via importance of perturbed feature) on 200 organ offers.
Table 3: Consistency of interpretations.
METHOD
CONSISTENCY
BC WITH LIME INVASE WITH LIME
46.8%
41.7% 7%
ITANNSPLANT
85.0%
7

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Table 3 shows that iTransplant is highly consistent while BC/INVASE with post-hoc interpretation via LIME [34] barely exceeds random. As result, iTransplant can be used understand clinical decision-making, while the low consistency of post-hoe interpretations for BC and INVASE render them inappropriate for this purpose.
iTransplant benefits from the learning capability of neural networks to perform comparably with black-box models while maintaining interpretability of the learned decision-makinng policies. This makes iTrnnsplant a powerful tool to investigate the decision-making policies in organ transplantation.
5.2 Investigative Experiments
As shown above, iTransplant can effectively learn transparent policies from the decision-makingg history of clinicians while achieving significant performance improvements to white-box models. Here, we demonstrate how iTransplant can be used investigate clinicians' policies organ offer acceptance from three perspectives:
1: Which criteria are important for clinicians' decisions organ offers?
2: How does clinical practice change for patients with different characteristics?
3: What variation in practice exists between transplant centers?
Based on the similarity in organ acceptance rates and total numbers of organ offered, two transplant centers with ID codes 16864 and 19034 are selected for these investigative experiments. For convenience, we denote these centers as center and center B, respectively. For the first two experiments, transplantation data from center A is used, while for the last one, learn separate policies for each center to explore the policy variation between the two centers.
iTTansplant to
Discovering important match criteria The relative importance of different match criteria can be measured by the normalized policy signature ý p/||p||2- With the distribution normalized policy signature ý plotted in Figure 3, we are able to examine the learned policy at the population level. For most patients, as highlighted Figure 3, the donor age and weight, presence of hepatitis (HBV) or C (HBC), can-heart-beating donors, cause of donor death (donation after natural death), and high percentages of macrosteatosis (MaS) have negative contributions to the acceptance organ offers. These observations are in line with risk factors reported in medical literature [33, 19] and guidelines [6].
MELD
MELD-Na
Donor Height
Donar Weight
Donor
Local Donor
Regional Donor
National
Positive
HBV Positive Donor
African American Donor
Female
Donation After Natural Death
Non-heart-beatie Donation
Medium Degree Ma5
High Degrea
Medium Degree
High Degree MIS
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
Figure 3: Distribution of normalized policy signature X (x-axis) over match criteria (y-axis), see the Appendix for explanation. Criteria with weights between the dotted blue lines could be ignored with no more than 0.1 % loss in the average precision score.
On the other hand, organs from local donors are preferred by clinicians while the organs shared at regional or national levels are more likely to declined. This is supported by research on the impact of cold ischemia time (CIT), which is often consequence of the organ origin [25, 9, 10]. Also,

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Figure 3 shows that, for given patient, higher score from the model for end-stage liver disease (MELD) [44] will lead to higher chance of organ offer acceptance. As an indicator for 3-month mortality of hospitalized patients, a high MELD score suggests greater severity of the liver diseases, which may potentially encourage clinicians to accept the offered organ.
Identifying patient-specific organ preferences of clinicians Here, we use iTransplant to investi- gate the patient-specific organ preferences of clinicians and how their deeiisonn-mainn is affected by patient features. We cluster the patients in the test set for center A by applying the KMeans algorithm to the latent space Z. The number of clusters is determined to be six such that the between- cluster variance of policy signatures is maximized. We find that patients in different clusters have significant deviations patient features. In Figure 4. we provide an overview of the policies for patients in cluster and 2 displaying the deviation in policy with respect to the averaged policy signature p for all patients (see the Appendix for detailed discussion).
policy signature indicating differences in clinical practice due to specific
Dencr Ape
HELD
Damor Age
HELD
Doner Helg
Degree HBS
Doner Height
Degroe NS
Weips
Meduum Seyree
Weitt
Desroe
Dentr ALT
Nedium Cagree Vas
Mediur Dagise Mad
Boniy
beoting
seating
Segional
Jreakis faw Natural Dwinh
Regional
Beration firre Naturn Daath
Nadonal Donth
Conor
Vetenal Donth
Ferrai Dentr
cV Bor Conor AMtes American Donor
HCV teettue Doner Imican American Dener
(a) Patient cluster
(b) Patient cluster 2.
Figure 4: The deviations of policy signature Ap p f of two patient clusters.
Organs procured from regional donors are significantly more likely to be accepted for patients in cluster 2 than cluster 1 (Figure 4(a)). One significant difference between clusters 1 and 2 is whether patients have hepatocellular cancer (HCC). cluster 2, 62. 1% of patients are HCC positive with a MELD score below 20, while only 1.4% of patients in cluster have both low MELD scores and the positive HCC status. Organs from regional donors typically have increased CIT compared to local donors. Prolonged CIT significantly reduces graft and patient survival, and thus organs from local donors are typically preferred (Figure 3). However, HCC positive patients with low MELD scores (< 20) are able tolerate longer ischemia times without significant impact on graft survival [24), explaining the increased weight for regional donor (Figure 4(a)).
To illustrate the advantage and effectiveness of i'Transplant in learning individualized policies for different patient groups, the policies learned by iTTransplant and by logistic regression method are compared in the Appendix.
Discovering variations in polices across transplant centers Here, we illustrate the use of iTrans- plant as a tool for discovering patient-level policy variations across transplant centers. To achieve this, policies A and B are trained with organ offer data from center A and B, respectively, and are compared on the test set for center A. The probability of offer acceptance predicted by policies A and # B was similar when averaged across all patients (difference: 0.005). However, there were significant differences on per-patient basis, with mean absolute error of 0.11 + 0.14, resulting in different decisions for many patients. We identify a major divergence (predicted probabilities of offer acceptance are 0.72 and 0.21 for A and B respectively) of these two policies for an accepted organ offer associated with 38 year old patient with high MELD score (39.0). The organ offered to this patient comes from a 53 year old donor with aspartate aminotransferase (AST) test value of

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22.0, and the donor AST value and donor age were found to be two important factors related to such divergence in the considered policies. To demonstrate how this divergence policies # and # affects the patient outcome, we assess the counterfactual impact of two donor characteristics on the organ offer acceptance probability by altering the age and AST test value of the donor (Figure 5).
1.0
1.0
Center
0.8
0.0
PIAcceptanco
PIAcceptance)
0.6
0.6
0.4
0.4
0.2
0.2
Center
Center
0.0
0.0
250
30
40
Donor AST
Donor Age
(a) Counterfactual impact of donor AST test value.
(b) Counterfactual impact of donor age.
Figure Patient-level variations in policies from different transplant centers.
The AST test value is indirect measure of liver cell integrity, with higher value in a donor suggesting more severe damage and therefore potentially worse post transplant outcome. Both policy A and # show preferences for organ offers with lower AST test value. However, for the considered patient, policy 7 is more tolerant to changes in donor AST test results. The low AST test result of the donor leads
significantly higher likelihood of offer acceptance compared policy A (Figure 5())). We find similar variations when examining the impact of donor age on the offer acceptance probability. While policy A has a high chance of accepting the organ offer from young donor, the acceptance rate decreases steadily as the counterfactual donor age increases. For the policy a the acceptance rate is largely insensitive the donor age until the age of the donor exceeds 30.
With the counterfactual impacts donor features plotted in Figure 5, we can intuitively observe the variations in transplantation practices in the investigated transplant centers. This demonstrates the potential of iTransplant as a tool for clinicians to examine their own decision-making policies with existing transplantation data and the impact of changes to patient and donor characteristics.
6 Conclusion
In this paper, propose individualized transparent policy learning framework iTransplant to understand the organ offer related decision-making of clinicians. The individualized policy learned by iTransplant shows significant advantage compared global policy from logistic regression, highlighting the need to learn personalized policies. With real-world liver transplantation data, we conducted three investigative experiments demonstrating the use of iTransplant discovering patient-level variations in transplantation practice and thus informing iterations of clinical decision support tools. While our experiments demonstrate that most decisions can be explained by clinical features recorded in observational datasets, it is likely that some decisions were made based on factors that were not considered. Future work could explore the impact of including additional match criteria and contextual information concerning unit activity and outcomes.
tool for
Acknowledgements
This work was supported by Cystic Fibrosis Trust, Alzheimer's Research UK, the US Office of Naval Research (ONR), and the National Science Foundation (NSF, grant number 1722516). We thank the anonymous reviewers for their comments and suggestions. We thank the clinicians who participated our survey. We thank Dr. Brent D. Ershoff for valuable discussions and counsel on the OPTN dataset and the observational features and decision criteria considered in this work. This work is based on the liver transplant data from OPTN, which was supported in part by Health Resources and Services Administration contract HIHSH250-2019-0000
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13


Reviews:
[{"review_id": "lyxcy5xqDyo", "reviewer": "Reviewer_PPKT", "summary": "Develop a data-driven policy learning framework to infer clinical decision making processes around the applied question of organ transplant offers and acceptances (organ donation data). Use this approach to better understand the underlying decision processes with mroe interpretability than \"black box\" methods, but more personalization and accuracy than standard logistic regression methods.\n", "questions": "", "limitations": "", "rating": 7, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Note that I already reviewed a prior version of this paper that was submitted to ICML. I overall rated the manuscript favorably then and will mostly reiterate my assessment here:\n\nGood description of a high-stakes but variable clinical decision problem and a policy modeling framework to better understand sources of that variability.\n\nSince the goal is not achieving a particular measurable outcome (e.g., prediction accuracy), not as clear how to judge whether the interpretability of suggestions here offers decision makers much more value than they could get from logistic regression models.\n\nArgues that logistic regression is too general a model vs. a personalized one, but couldn't local site/physician features and interaction terms be added to a basic logistic regression model to achieve similar benefits?\n\nDecent survey of related studies understanding clinical decision making factors, though with varying purposes.\nIn addition to review of different technical approaches, useful review of the live decision making process. I was aware of the MELD prioritization scheme for liver transplant offers, but not aware of the variability in organ offer acceptance rates that the authors focus their modeling on here.\n\nWell laid out description of decision policy modeling framework, and reference to how that fits in the real-world process.\n", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "Hard to objectively compare the proposed method vs. existing methods.\nBetter understanding of decision drivers and risk assessment could significantly influence organ transplant practices. Misinterpreting correlations found through these approaches as causal could negatively impact transplant decisions without clear guidance on interpretation of findings.", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "1"}, "extra_text": {"ethical_concerns": "No major issues. Research unpacks potentially high stakes transplant decisions that could inadvertently bias against underrepresented groups. Overall the purpose to gain better interepretability into black box physician decision making processes may be beneficial to expose likely existing biases in routine (but variable) practice.\n"}}, {"review_id": "RSFTZ4iaB7n", "reviewer": "Reviewer_Q9SD", "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.", "questions": "", "limitations": "", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "This paper is very well written and mostly easy to follow. Their main contributions are relatively novel, and they do improve on prior work in some meaningful ways. In particular: Their model is useful for illustrating how individual-level (and hospital-level) organ accept/reject decisions are made, and which latent features are most salient to these decisions. This extends prior work with \"black-box\" models, which provide no intuition as to the decision making process. This interpretability comes at very little cost in accuracy, since their model performs nearly as well as the best \"black box\" model they compare against.\n\n\nSome general comments & questions follow:\n\n- The latter part of the pipeline is somewhat unclear: what role does the MoE module play, and why use an MoE rather than a single network? (It seems that this module learns the transform \\mathcal{T} and weight vector(?) \\rho, however this is not clear.)\n\n- In general, there are several design decisions that seem arbitrary, yet are very important to the model design. I think it would substantially improve the readability and impact of this paper if you clarified these design choices when introducing the model. Some of these choices include:\n\n    - Why choose the Boltzman distribution / logit model in (1), as opposed to some other model? (This model is similar or identical to a multinomial logit, which would be natural appropriate if a patient needs to choose between multiple organs. But in your case, a patient is choosing whether or not to accept a single organ, or wait for another option. So what is the intuition behind this model?)\n\n    - Why use the MoE module as opposed to something else?\n\n    - Why is consistency an important criteria? (Eq. (2)). There are many reasons my preferences for donor organs would not be smooth -- for example, I may be likely to reject an organ from a donor with a particular antibody that I am incompatible with, even though this donor may be a good fit in all other aspects. (So this organ is close to a desirable organ in feature space, but very far from the desirable organ in decision space.)\n\nTo be clear, I'm not saying that these choices are bad. I just think you need to justify them.\n\n- Some comments on the application & related literature. Overall, since this is mainly an applications paper, I think there needs to be more discussion on related application-focused literature as well as more explanation of the experiments.\n\n    - There is a large body of literature on this topic, and I suggest you engage with it more in your paper. For example, this paper (https://aasldpubs.onlinelibrary.wiley.com/doi/full/10.1002/lt.24113) has a very similar goal to your paper, with a more applied focus. They discuss two main criteria of interest to organ patients: graft failure and disease transmission. It is likely that these factors also play a major role in the decisions you are aiming to model in your paper. If you do not use this domain knowledge, you're just making the task of predicting donor behavior harder. Even if you do not use this domain knowledge, you should certainly cite it.\n\n    - The organ accept/reject decisions are strongly dependent on both the waitlist prioritization (who has the highest priority to receive an organ) and the compatibility criteria (when is an organ compatible with a patient). At very least, these policies will make certain features in your dataset very dependent. For example, certain patient types will never be offered certain types of donor organ. Please discuss how these policies impact your analysis.\n\n", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "I'm a bit concerned about the interpretation and use of the methods proposed in this paper, and I think more discussion is needed. \n- What is the intended use of this model? \n- What is the interpretation of your results? (Especially the normalized policy signature in Fig. 3 and the patient clusters.)\n- Is this analysis meant to influence future decisions? And if so, how does this differ from state-of-the-art decision tools for organ allocation? \n    - If so, what are the risks for using this method?\n\nThese are issues because of the sensitive and applied nature of this paper, where the prediction task at hand is literally tied to human life and death.", "review_text": "", "extra_scores": {"ethical_concerns": "See \"Limitations and societal impact\". \n\nI don't want to flag this paper for ethics review. But I would certainly like the authors to address the questions I raised.\n", "needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "2"}, "extra_text": {}}, {"review_id": "5p5VzO_9-sg", "reviewer": "Reviewer_oN9u", "summary": "In this paper, the authors propose a policy learning strategy that is patient-driven and interpretable. They focus on the case of organ transplant, not predicting matching but rather whether an organ will be accepted by a clinician for their patient. The authors show that their method perform on par with other approaches, with the added benefit that the decision space is interpretable and can be investigated.", "questions": "", "limitations": "", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Originality\n---------------\nThis is an interesting combination of different ML components to provide personalized policies. Technically, those components are not novel, but I find their combination interesting. The overall idea is quite similar (in philosophy) to that of Koh et al., 2020 (https://arxiv.org/abs/2007.04612). This parallel could be made explicit in the text.\n\nQuality\n----------\nThe experiments are mostly described in the supplementary materials. I have a few questions regarding some of the data and modeling choices:\n- \"For the experiments in this paper, we select rejected organ offers of twelve cases, covering 87.7% of all rejected offers\". It feels like the data is selected based on parameters that are well represented in the criteria space. While I understand the reasons for not including all offers, this makes me question the broader applicability of the method. Can the authors comment on a potential circularity here, that might reduce the application to other settings or new patients? \n- In the same way, the features for donors and patients are selected based on the criteria.\n- The criteria space was selected based on domain knowledge. Do you mind expanding on who was consulted to provide those criteria? Was this set defined a priori and then used, or were multiple sets tried? If the latter, was there an effect on performance of the selected set?\n- In the selection of hyper-parameters, the authors mention that they impose models to have similar learning capacities. This seems like an unnecessary constraint, especially given the diversity of approaches considered. Have the authors tried to optimize each technique, and maybe then compared the required numbers of parameters or CPU/GPU resources? While I understand the reasoning, a more fair comparison for overall usability would optimize each technique \"independently\" of the others, and then other parameters (such as computational expenses) can be taken into account in the comparison.\n- Can the authors comment on the lower performance for center 6820?\n- Supplementary Table 2 includes interesting results, showing that LASSO can perform similarly to the selected method when using (X,O) as input. Can the authors comment on their choice of including results in the main vs supplementary table?\n\n\nClarity\n---------\nThe paper is overall well written. I would encourage the authors to revise some of the phrasings, that I found a bit convoluted. In general, simpler sentences would probably help the reader.\nI also found that many of the experimental details were missing in the main text. I understand that these are part of the Supplementary Materials, but splitting the main text from the supplemental is a critical part of the writing, that I believe could be improved here (e.g. remove or make a bit more compact some of the intro and related works, bring in data descriptors, split and training/testing strategies at the high-level).\nAnother main source of confusion for me was that the space of criteria was not directly described as manually crafted based on domain knowledge. This seems to be critical for the interpretability of the method: a limited set of criteria, that are human-understandable. This information should be available early in the text, and discussed for its limitations.\n\nSignificance\n-----------------\nThe authors mention that their work could help understand clinical decisions, and understand variations across centers. This seems promising but I was wondering whether those were desired by the clinical community? I also missed examples of other problems that could benefit from the method.", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "The authors provide a brief conclusion in the main text, and a short paragraph on limitations in the supplementary. I believe that the work is making a few assumptions and I would encourage the authors to explicitely discuss these in the main text. I have identified the following limitations (in addition to previous comments):\n- The authors do not comment on a validation with clinicians as to whether the proposed approach would be useful for them to understand the decision making process.\n- There is no validation that the proposed interpretability is useful. While SHAP and LIME are shown to provide results that are not consistent (which is not surprising given that these methods are not robust to perturbations), it is unclear whether clinicians would benefit from interpretability (in the sense that it helps them in achieving their goal), or that this technique in particular is answering their needs in terms of interpretability.\n- Is the method able to handle missing data? How about other methods?\n- Can the authors comment on some of the assumptions made, including linearity in the criteria space?\n- The clustering and the investigation per center are interesting and convincing. However, there is little validation of the results, which are susceptible to confirmation bias. Can the authors comment or add as a limitation?", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "3"}, "extra_text": {}}]

Author rebuttal:

Meta-review: Reviewers' scores and sentiments were remarkably consistent and positive toward this work.  The approach taken to address a high-stakes problem seems appropriate, and reviewers appreciated both that approach and its validation.  Still, reviewers -- especially Q9SD and oN9u -- did have outstanding questions regarding details about the methodology and about the experimental validation, and I would encourage the authors to update their work to reflect answers to those questions (many of which were given in the rebuttal).
Decision: Accept (Poster)