PCPs: Patient Cardiac Prototypes to Probe AI-based Medical Diagnoses, Distill Datasets, and Retrieve Patients
Authors: Dani Kiyasseh
Tingting Zhu
David A. Clifton
Venue: Accepted by TMLR ()

Abstract: Clinical deep learning systems often generate population-based and opaque medical diagnoses. This is in contrast to how primary care physicians make decisions, often adapting population-based protocols to the unique patient under consideration. Inspired by the workflow of such physicians, we develop a framework for learning embeddings, referred to as patient cardiac prototypes (PCPs), which capture information that is unique to an individual patient's electrocardiogram (ECG) data. Through rigorous evaluation on three publicly-available ECG datasets, we show that PCPs allow researchers to inspect why a particular diagnosis was made. We also demonstrate that PCPs are effective dataset distillers, where they can be used to train a model in lieu of a dataset orders of magnitude larger to achieve comparable performance. We show that PCPs can also be exploited to retrieve similar patient data across clinical databases. Our framework contributes to the development of transparent and patient-specific clinical deep learning systems.

Published in Transactions on Machine Learning Research (01/2023)
PCPs: Patient Cardiac Prototypes to Probe Al-based Med- ical Diagnoses, Distill Datasets, and Retrieve Patients
Dani Kiyasseh
danikiy SomikiyShotmail.oom com
Deportment of Computing and Mathematical Sciences California Institute of Technology Pasadeno, CA, USA
Tingting Zhu, David Clifton Department of Engineering Science University of Onford Oaford, UK
grst.last@eng.oom
Reviewed on OpenReview: htttps///openeewee net/ forum? id= [ WMCMBO
Abstract
Clinical deep learning systems often generate population-based and opaque medical diag- noses. This is in contrast to how primary care physicians make decisions, often adapting iouulation-based protocols to the unique patient under consideration. Inspired by the work- flow of such physicians, we develop a framework for learning emboddings, referred to as pa- tient cardiac prototypes (PCPs), which capture information that unique to an individual patient's electrocardiogram (ECG) data. Through rigorous evaluation on three publicly- available ECG datasets, we show that PCPs allow researchers inspect why particular diagnosis was made. We also demonstrate that PCPs are effective dataset distillers, where they can be used to train model in lieu of a dataset orders of magnitude larger to achieve comparable performance. We show that PCPs can also be exploited to retrieve similar patient data across clinical databases. Our framework contributes to the development of transparent and sppeeit clinical deep learning systems.
1 Introduction
Primary care physicians often account for the complex mosaic of patient's demographics and physio- logical state by adapting population-based protocols to the unique patient under their care (Hamburg & Collins 2010 Such patient-specific approach is in contrast to, for example, randomized controlled trials, long considered the gold standard of evidence in medical research (Cartwright, 2007), where findings are population- baased and may overlook patient-specific nuances (Akobeng) 2005). These characteristics are all too common in deep learning systems which, despite their success in automating the diagnosis of medical
conditions such as cardiovascular diseases (Galloway et al.] 2019: Attia al. 2019a Ko et al. 2020 continue to generate population-based and opaque diagnoscs.
We believe that, akin to primary physicians, clinical deep learning systems can benefit from explicitly incorporating information about individual patient's data into their decision making, For example, the explicit dependence of deep learning- barning-based predictions inspect why such predictions were made. Operating al the patient level also allows for the retrieval of patient data, where a clinical database is searched in order to retrieve data that are most similar to the data of an existing patient. Such retricval capabilities can allow physicians compare the discase and treatment trajectories of similar-looking patients, and enable medical educators to leverage up-to-date real- world evidence means of teaching the next generation of medical students.
unique patient data can enable researchers to

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Previous studies have attempted to capture information that is unique to individual patient's data. This has primarily been achieved through graph neural networks (T.u & Uddin 2021 Hernándey- et al. 2022) where each node in the graph reflects a unique patient and the edges reflect the similarity of patients (Sharafoddini et al., 2017 Pai & Bader. 2018 Pai et al.) 2019). These approaches, however, assume that an individual patient's 's data are already succinctly represented by a set of features and that the similarity between patients are known priori, often reflected through an adjacency matrix. We instead learn these compact patient representations from scratch which can subsequently be used, for example, to discover the adjacency matrix. Driven by the recent success of contrastive learning (Cheng et al.) 2020), researchers have focused on learning patient-specifi representations of cardiac signals as a pre-training step (Kiyasseh et al. 2021b) or on learning prototypes of patient groups, such as those sharing the same sex, age, and disease class altributes (Kiyasseh et al. 2021a). Protolypes that have been introduced in the past are either exclusively limited to the few-shot learning application (Snell et al. 2017 Sung et al.) 2018 or to clustering data points an unsupervisod manner (Li et al.] (021). In contrast, we develop a supervised contrastive learning framework for the learning of prototypes with multiple use-cases.
Our main contributions are fourfold. First, to explicitly incorporate information about a patient's data into a deep lcarning system, we develop a framework that Iearns embeddings which we refer to as patient cardiac prototypes (PCPs) via supervised contrastive learning ($2.2). p. By design, PCPs capture information unique to an individual patient's electrocardiogram (F.G) data ($.2)) Through rigorous evaluation on three publicly-available ECG datasets, we demonstrate that PCPs allow for the probing of deep learning- based predictions, enabling researchers to inspect why a particular prediction is made ($4.4). This can contribute to the development of Imanspaent clinical deep learning systems and instill trust in clinical stakeholders. We also show that PCPs are effective datasct distillers, where they can be used to train model and match the performance of one trained on the full dataset ($4.6). In the process, we demonstrate that PCPs outperform state-of-the-ard core-set construction methods. This capability the potential to reduce the amount of resources required to train deep learning systems. Lastly, we show that PCPs can reliably retrieve similar (and dissimilar) patient data across distinct clinical databases ($4.7), thereby potentially facilitating the provision of education by medical educators to the next generation of medical students.
2 Patient cardiac prototypes: motivation and design
2.1 Leveraging spatial and temporal invariances
Recent research has demonstrated the benefit of learning representations of cardiac signals that are invariant to spatial and temporal sources of variability while pre-braining on large-scale cardiac datasets (Kiyasseh) et al. 2021b). An invariance is an aspect of the input data which, when changed, does not alter the information exhibited by that data. That study demonstrated that attracting representations of different ECG leads to one another or attracting representations of lemporally-adjacent ECG signals to one another (e.g., on the order of seconds) can allow deep learning systems to perform cardiac arrhythmia classification with fewer training data points than would otherwise have required. Inspired by those findings, we decided, in this study, to leverage the same spatial and temporal invariances to learn representations (PCPs). We assume that data that belong to the same patient reflect the same type of information, otherwise known intra-patient invariance (see Appendix A for additional motivation).
2.2 Learning patient cardiac prototypes
Notation Let us assume have dataset, D= = (xa, V/ I.N comprising N instances, æ, and cardiac arrhythmia labels, y. corresponding to total of Perain patients in the training set. Each patient is associated with N/Strain > 1 instances. This could be due to the provision of multiple medical tests during the same hospital visit or several visits. We also define a learner, fo : x E Rº + h E RF, parameterized by 0, that maps a D-dimensional instance, æ, to an E-dimensional representation, h. We aim to learn a sot of embeddings, each of which efficiently summarizes the cardiac state of a patient. To that end, we associate each patient in the training set with a unique, learnable embedding, p E R.E to form the set of embeddings,
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== (pylfer'" Perai Hereafter, we refer to such embeddings as patient cardiac prototypes (PCPs). We next explain how to learn PCPs.
Contrastive learning We use the contrastive learning framework which, in short, consists of a sequence of attractions and repulsions between reprosentations of instances. The idea to attract representations of instances of the same patient to the single PCP of that same patient, and to repel them from the PCPs of the remaining patients. Formally, associated with the k-th patient to be similar to the k-th PCP, Pk: and dissimilar from the remaining PCPs. Py: j + k (see Fig. Ib. To achieve this, we optimize variant of the InfoNCE loss (Oord et al. 2018 (equation)) D. Intuitively, it penalizes the learner for placing less probability mass on the similarity, s ((hi. Pk); of the representation and prototype pair that should be most similar (based on patient ID) than on the similarity of other pairs, of which there are Perein We quantify the cosine similarity between such pairs with a temperature parameter, T.
encourage the representation, h = fo( (z))),, of an instance, Z
B Σ log
h; Pj
1
LNCE =
Ph;
(1)
[/: /hilllppl
As result of this many-to-one mapping from reprosentations to PCP, the latter will become invariant to intra- intra-palient differences present in the data. For context, it is these protolypes, P, which are presented in Fig. 2 (left). This outcome is desirable only if we assume that such intra-patient differences point to the same underlying physiological state of the patient.
2.3 Incorporating patient cardiac prototypes into diagnosis pipeline
Equipped with PCPs, we direct our attention to the question of how do we exploit PCPs to generate diag- noses? Before addressing this question, we note that neural network parameters are often deterministic; they are held constant during inference. Therefore, when making a prediction (e.g., medical diagnosis), a network depends almost exclusively on the instance. Although an instance likely to reflect patient information, a network does not explicitly exploit such information. In light of this, we design a framework, inspired by hypernetworks (Ha et al. 2016), in which subset of the network parameters are explicitly conditioned on patient information.
Medical diagnosis with hypernetworks A hypernetwork, a neural network and of itself, generates parameters for another neural network. Formally, a hypernetwork is a function, Io : R + w RExC parameterized by Φ that maps an E-dimensional representation, h, to matrix of parameters, w, where C is the number of class labels (i.e., cardiac arrhythmia categories). The output parameters, w, can be used to parameterize linear classification head, Pa h E R - y E IR,, which maps E-dimensional reprosentation, h, to an output probability distribution, To explicitly condition the parameters, on patient information as we had initially desired, we exploit PCPs differently during the training and inference stages of the framework, as outlined next.
Retrieving PCPs during training During the training stage, each representation, h; fe of an instance, It, serves multiple purposes (see Fig. [ left). First, it is attracted to its corresponding PCP, Pk, as outlined earlier. To do so, we optimize the InfoNCE loss. Second, it is used as an input to the hypernetwork to generate instance-specific parameters, wi = (h.)... Third, the representation is input into the classification head, Pw, as is usual with neural networks. Given the ground-truth disease class, e, of each instance in a mini-batee batch of size B, we can optimize the categorical cross-entropy loss (LCE). In summary, during the training stage, we learn the parameters of the feature extractor (0), the hypernetwork (Ø), and the PCPs ((p)) (leann)) in an end-to-end manner by optimizing the combined loss (Lrombined).
B
LCB = log pw. (y): = c(h2))
Σ
Lcombined = LCE + LNCE
(2)
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Feature Extractor
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Figure 1: Training and inference stages of cardiac arrhythmia diagnosis with patient cardiac proto- types. (traiming stage) representation, h, of instance, z, belonging to data from patient & (a) fed into hypernetwork, Pe. to generate parameters, for linear classification layer, pw, (b) input directly into pa to output class probability distribution, and (c) encouraged be similar to corresponding patient cardiac prototype (PCP). Ps- This is akin to learning the features of node in graph from scratch. (inference stage) the representation of an instance associated with an unseen patient retrieves the nearest PCP, pa (see graph analogy), which then input into the hypernetwork. This generates linear parameters for classification.
Retrieving PCPs during inference During the inference stage, propose several modifications to the pipeline (see Fig. [ right). Since palients in the inference stage do nol overlap with those in the training stage (by design), we no longer attract or repel each representation, h4, from PCPs. Instead, each reprosentation scarches through the set of PCPs, P, and retrieves the single PCP to which it closest, Ps = argmax,, s(h,, where s is some similarity metric such as cosine similarity.
This similarity between patients is akin the cross-attention mechanism used in Transformer architectures (Vaswani et al.] (017)) to edges connecting patients in graph (Fig. 1 right). While it is possible to retrieve single instance (as opposed to PCP), doing has several drawbacks. First, removing the PCP from the pipeline would make it difficult to reason at the patient level, which can be useful when probing the errors of system ($4.4). Second, PCPs can conceptually be extended to multiple data modalities, thereby succinctly summarizing the clinical state of a patient more broadly. For the implications of multi-modas prototypes, please refer the Discussion. Moreover, from a computational standpoint, searching through the full datasct to retrieve a single instance is more demanding than scarching through the PCPs, which can be orders of magnitude smaller in size. This can introduce unwanted latency during inference. We nonetheless conducted such experiments and found minimal change in the diagnostic performance of the system.
Once we retrieve the PCP, Pt, it is used as an input to the hypernetwork, parameters, W; = (px), for each unseen instance the held-out set of data. In other words, we generate parameters conditioned on the single PCP associated with the patient the training set that is decmed most similar to the patient observed during inference. summary, the final diagnosis underpinned by this retrieval and parameder-generatioo process.
As a result, we generate
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3 Experimental design
3.1 Electrocardiogram datasets
In this study, we experimented with three publicly-available datasets of electrocardiogram signals and cardiac arrhythmia labels. CPSC (Alday al. 2020) consists of 12-lead ECG recordings from 6,877 patients alongside nine cardiac arrhythmia labels: AFIB, I-AVB, LBBB, Normal, PAC, PVC, RBBB, STD, and STE. Chapman (Zheng et al., 2020) consists of 12-lead ECG recordings from 10,646 palients alongside four high-level cardiac arrhythria labels: AFIB, GSVT, Simus Bradycardia, and Sinus Rhythm. PTB-XL (Wagner et al.) 2020) consists of 12-lead ECG recordings from 18,885 pationts alongside 71 different types training and evaluation
of annotations provided by two cardiologists. We followed a previously-establishee protocol (Strodthoff et al., 2020p where we leveraged the diagnostic class labels: Conduction Disturbance (CD), Hypertrophy (HYP), Myocardial Infarction (MI), Normal (NORM), and Ischemic ST-T Changes (STTC). We altered the original setup to only consider ECG segments with one label assigned to them and converted the task into binary classification problem (NORM vs. Rest). Across all datasets, we split patients randomly into training, validation, and test sets, ensuring that there was no patient overlap between sets (see Appendix Bh.
3.2 Description of tasks and baselines
We aim to demonstrate the utility of patient cardiac prototypes in achieving three distinct tasks: cardiac arrhythmia classification, dataset distillation, and patient retrieval.
Cardiac arrhythmia classification To achieve this task, we follow the previously-outiined diagnosis pipeline (Fig. D. We also conduct an extensive set of ablation studies where we investigate the effect on per- formance of (a) retrieving a different number of patient cardiac prototypes during the inference stage ($4.3.1) (b) learning prototypes in a purely supervised manner (Lce only) ($4.3.2), and (c) learning prototypes in a purely contrastive manner (CNce only) (.3.3). Additional details are provided in Appendix D
Dataset distillation In light of our interpretation of patient cardiac prototypes efficient descriptors of the cardiac state of patient, we leveraged them for the task of dataset distillation. Here, compact and potentially synthetic dataset is used to train a model that matches the performance of one trained on the the full dataset, which is orders of magnitude larger (Wang et al. 2018 Cazenavette et al. (022). In our context, we first learned the patient cardiac prototypes which were used to subsequently fit a machine learning model (e.g., support vector machine). We then evaluated the performance of this model on representations of unseen cardiac signals, as with the cardiac arrhythmia classification task described above. We benchmarked PCPs against. state-of-tho-art methods ($4.6) which construct compact subsets of data (core-scts) which we refer to as Lucic (Lucic et al. 2016), Lightweight (Bachem et al. 2018), and Archetypal [Mair & Brefeld 2019
Patient retrieval By virtue of capturing information that is unique to a paticnt's cardiac data, we hypoth- esized that patient cardiac prototypes might also have the potential to search through an unseen corpus of data and retrieve patients whose cardiac attributes (e.g., disease class, heart rate, etc.) match those of the patient cardiac prototypes. To achieve this, we treat a patient cardiac prototype as a query and calculate its cosine similarity to all the representations of cardiac signals for patients in the unseen corpus. When aiming to rieee similar patients, we inspect the pairs of patients with a higher cosine similarity ($4.7)
3.3 Evaluation metrics
For the tasks of cardiac arrhythmia classification and datasct distillation, we evaluated the perfor- mance of models by calculating the area under the receiver operating characteristic curve (AUC) of their predictions on unseen cardiac signals in a held-out set of data. For the task of patient retrieval, we quantify the proportion of data points retrieved which are in fact relevant. When aiming to retrieve similar patients, we define relevance as data points whose cardiac attributes match those of the query patient cardiac prototype. In contrast, when aiming to retrieve dissimilar patients, define relevance data points whose
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attributes do not match those of the query prototype. These equivalent to calculating the precision and negative predictive value (NPV) of the retrieved data points, respectively. Additional details can be found in Appendix
4 Experimental results
4.1 Patient cardiac prototypes distinct and distinguish between cardiac arrhythmias
To inspect the type of information captured by our learned patient cardiac prototypes, we visualized them via UMAP (McInnes al. 2018) (see Fig. 2 left). We found that patient cardiac prototypes
distinet from one another. This is evident, qualitatively, by the lack of collapse of the PCPs to a select few points (Fig, 2 left), favourable finding in our context. To see why, note that during inference on an unseen cardiac signal, prototypes are explicitly input into a hypernetwork that generates parameters enabling a cardiac arrhythmia classification (sec Fig. I right, and Methods). Thorefore, if prototypes were to collapse to a single point, then the same parameters will be generated regardless the patient data input, diminishing their expressiveness. A more quantitative approach to supporting this claim is provided in the next section. We also found that PCPs do indeed reflect cardiac arrhythmia information. This is evident by the visible separability of the UMAP projections based on the cardiac arrhythmia categories.
4.2 Patient cardiac prototypes capture information unique to patient's ECG data
We explored the extent to which patient cardiac prototypes capture information unique to an individual patient's data. To do so quantitatively, we calculated the (Euclidcan) distance between PCPs and represen- tations of cardiac signals, belonging either the same patient (PCP to Same Training Patient) or different patient (PCP to Different Training Palient). We present the distribution of these distance values for the Chapman dataset (Fig. 2 right).
We found that PCPs do indeed capture information that is unique to a patient's 's ECG data. This is evident by the smaller distance values of the PCP to Same Training Patienl group (average = 4) than the PCP to Different Training Patient group (average 9) (Fig. 2 right). This finding suggests that PCPs are twice as similar representations of cardiac signals from the same patient than those from a different patient. Furthermore, for our proposed retrieval mechanism work during the inference stage (see Fig. D. the distance between representations of unscen cardiac signals and PCPs (Fig. 2 right, PCP to Volidation Pulient) must be reasonable and on the same order of magnitude. We found that these distance values
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Figure 2: Patient cardiac prototypes can distinguish between cardiac arrhythmias and capture infor- mation unique to individual patient's ECG data. (left) UMAP projection of the PCPs with colours reflecting the four cardiac arrhythmia labels, Simus Rhythm (SR), Sinus Bradycardia (SB), GSVT, and Atrial Fibrillation (AFIB). (right) Distance between PCPs and representations of instances in the training set associated with the same patient annotation, representations in the training set associated with a different patient annotation, and represen- tations in the validation set..

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are indeed reasonable since they are the same order of magnitude the distance values of the PCP to Different Training Palients group.
4.3
Ablation studies
4.3.1 Effect of retrieval mechanism
During the inference stage, our framework is dependent on the retrieval mechanism (see Methods, Fig. 1. For example, to diagnose an unseen cardiac signal, we retrieve the single PCP that is closest to its representation. To appreciate this, note that the retrieval of an inappropriate PCP would have ramifications on the linear parameters that are generated by the hypernetwork, and in turn, the cardiac arrbythmia diagnosis. We therefore explored four variants of this retrieval mechanism which differ in the extent to which they leverage information unique to patient's s ECG data (see Appendix for details on variants, Fig. 3 left).
We found that incorporating imprecise information about an individual patient's ECG data hindered the generalization performance of the deep learning system. For example, the mean variant, in which all PCPs are simply averaged before retrieval, achieved AUC 0.65 irrespective the embedding dimension, which is significantly lower than that achieved by other variants of the retrieval mechanism. Incorporating some patient information resulted in a significant improvement in performance. For example, the similarity- weighted mean variant, in which retrieve weighted linear combination of PCPs, achieved AUC = 0.75 compared to 0.66 for mean E 64. This suggests that the PCP-derived similarity coefficients in the latter approach were beneficial, thus lending support to the utility of PCPs in the diagnosis pipclinc. When limiting the retrieval mechanism to retrieve a single PCP (nearest variant), we found that individual PCPs were still relevant for diagnosis. For example, at E = 64, similarity weighhtt mean and nearest achieved AUC z 0.75 and 0.89, respectively. Incorporating additional information from a subset of patients (nearest 10 variant) further improved performance, albeit in more marginal way.
4.3.2 Effect of prototype definition
We also explored whether it was even necessary incorporate prototypes into the deep learning system. To do so, replaced individual patient's prototype with the average reprosentation of that patient (mean representation), without having to learn prototypes with contrastive loss (Fig. 3 right). We found that mean representations capture information that is less unique to an individual patient's 's ECG data than do PCPs. This is evident by the smaller difference in performance across the variants of the retrieval mechanism when using mean representations relative to PCPs (Fig. 3 right). For example, the greatest difference in
Ncarest 10
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Figure 3: Ablation studies examining the marginal impact of the components of framework performance. Effect the retrieval mechanism variants on the performance the deep learning systems when using (left.) PCPs and (right) embeddings learned without contrastive loss, also referred to representations.

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the AUC 0.92 0.83 0.09 and AUC = 0.89 0.66 0.23, for the mean representations and the PCPs, respectively.
4.3.3 Effect of supervision
To examine the importance of supervision the learning of prototypes, trained variant of our framework without disease class labels, and subsequently used the prototypes for cardiac arrhythmia classification with k-nearest neighbours (k-NN). Here, reflects the number of prototypes retrieved during inference. We compare the performance of this approach (NCE Loss Only), which is akin to the work of Li et al. (2021), to that patient cardiac prototypes (Combiine Loss) (Appendix F Fig. S). expected, we found that prototypes learned without cardiac arrhythmia label supervision perform more poorly than those learned with supervision.
4.4 Patient cardiac prototypes allow for the probing of deep learning baseed diagnoses
The design of patient cardiac prototypes and their subsequent use in the diagnostic pipeline can allow machine learning practitioners to inspect why deep learning-basse based medical diagnosis was made. Specifically, we hypothesized that correct deep learning- based predictions were more likely to be associated with a retrieved PCP whose patient characteristics (e.g., disease class, age, etc.) matched that of the unseen cardiac signal. The inverse would hold for an incorrect prediction. The intuition that retrieving an irrelevant PCP, where relevance is based on matching patient characteristics, would lead to classification errors. We tested these hypotheses quantitatively and qualitatively.
Quantitatively, inspected the proportion of correct (incorrect) deep learning- predictions that were associated with relevant (irrelevant) PCP retrieved during inference (Fig. 4 left). As expected, we found that while 94% of correct predictions were associated with the retrieval of a relevant PCP, 74% of incorrect prodictions were associated with the retricval of an irrelovant PCP. We reaffirm the latter qualitatively by randomly identifying an incorrect prediction and inspecting the retrieved PCP (Fig. 4 right). We found that the patient associated with retrieved PCP and that associated with the unseen cardiac signal exhibited different characteristics (disease class: supraventricular tachycardia vs. atrial fibrillation, age 63 87, ventricular rate: 164 vs. 91). These findings demonstrate how PCPs can be used to conduct an error analysis and shed light why a misdiagnosis was made.
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Figure& 4: Correctness of deep learning-based predictions often depends on relevance retrieved PCP. (left) Proportion of predictions in which the retrieved PCP relevant to the unseen cardiac signal. (right) Incorrect prediction is associated with the retrieval of an irrelevant PCP. This approach facilitates error analysis and allows researchers to determine why individual patient's 's diagnosis was made.

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4.5 Patient cardiac prototypes outperform state-of-the- dataset distillation methods
We trained a model exclusively on the learned patient cardiac prototypes and evaluated on representations of unseen cardiac signals. We benchmarked patient cardiac prototypes against baseline methods when deployed on either raw cardiac signals or representations of such signals learned through framework (Table 1
We found that core-sets of raw instances, generated by baseline core-set construction methods, did not provide a sufficient. training signal to allow machine learning models lo achieve strong generalization ion performance. For example, on the Chapman dataset, Lucic, Lightweight, and Archetypal achieved AUC = 56.8, 56.6 and 54.8, respectively. We attribute this performance the low class scparability of the input features. However, the baseline methods continued to perform poorly even when provided with the opportunity to construct core-sets from representations learned via our framework. Recall that these representations are more separable along the disease class dimension (see Fig, 2 left and the associated performance in Fig. 3 For example, on the Chapman dataset, Lucic, Lightweight, and Archetypal achieved AUC = 57.8, 58.9 and 58.1, respectively. We found that PCPs outperforeed these state-of-hhe-art core-set construction methodss instead achieving an AUC = 88.7.
4.6 Patient cardiac prototypes are effective dataset distillers
To determine whether PCPs are effective datasot distillers, we compared the performance of a model trained 100% of the PCPs (F 1) to one trained on the full training set. To explore the extent which further distillation was possible, we randomly chose fraction, F E (0.05,0.1,00) 0.1, 0.2, 0.5], of the PCPs and trained machine learning model on that subset, while continuing to evaluate (Fig. We also depict the performance of these frameworks when trained on all instances in the larger, original dataset (horizontal, dashed lines), which several folds larger than the number of PCPs.
the same held-out set of data
We found that PCPs are indeed effective dataset distillers (Fig, 5). For example, when training on 100% of the PCPs (Deroin 6, 387), an SVM model achieved similar performance (AUC 0.89) one trained all cardiac signals in the training set (N 76, 614). Expressed differently, similar performance achieved despite 12-fold reduction in the number of training instances provided to the model. Such finding suggests that the PCPs are able to capture the most pertinent information in the dataset and neglect
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Table Patient cardiac prototypes outperform state-of-the-art core-set construction methods. For Chapman PTB-XL, we train an SVM, whereas for CPSC we train random forest (due to multi-label classification). The core-set size equals the total number of PCPs, Mean and standard deviation are shown across five seeds. Since the raw instances of PTB-XL are 12-lead ECG signals, they could not be used with an SVM.
Figure Patient cardiac prototypes are effective dataset distillers. Results are averaged across five ran- dom seeds. The horizontal dashed line depicts the perfor- mance of SVM trained on all instances (N 76644) in the training set. Despite 12-fold reduction the mim- ber of training instances (F Ataain 6, 387), the SVM achieved similar performance (AUC z 0.89) to one trained all instances.
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that which is redundant for solving the task hand. We also observed that more extreme distillation does not significantly hinder the performance of PCPs. For example, an SVM model trained with only 5% of the available PCPs (Nerain = 319) achieved AUC & 0.82. Expressed differently, this corresponds to a 7% drop in performance despite a 240-fold reduction (relative to training on all instances) the number of training instances provided to the model. Such a finding reaffirms the potential of PCPs as dataset distillers.
4.7 Patient cardiac prototypes reliably retrieve similar patient data
Quantitative evaluation We use PCPs as queries to search for and retrieve patient data, and present the precision and negative predictive value of the retrieved patient data function of the mathematical similarity between the PCPs and patient data, quantified via either Euclidean distance (Fig. t left) or cosine similarity (Fig. 6 right).
We found that patient cardiac prototypes reliably retrieved patients with a similar cardiac arrhythmia label. This is evident by the high precision achieved by PCPs at low Euclidean distance (high cosine similarity) values (Fig- G For example, > 90% of the pairs of patients that were deemed very similar to one another by our framework (i.e., dE < 6.2) exhibited the same exact cardiac arrhythmia label. We also found that the precision decays patients are deemed less similar to one another. For example, dg - 8.5, the Precision -> 0.3. This finding, which is promising and expected from reasonable similarity metric, is also exhibited by the precision curve in Fig. G
(right). Note that, in this case, the precision curve is flipped along the y-axis since the most similar patients are those with the largest cosine similarity.
Based on these findings, we can identify a threshold distance between pairs patients beyond which we are guarantcod a particular precision. This is useful for end-users looking for an empirical upper bound on the error. For example, in Fig. G (left), given a user-defieed acceptable level of precision (e-g., 0.90), we identify the threshold distance (e.g., dg 6.2) below which patients have high probability of being similar, This naturally lends itself to region of similarity, where patients identified as being similar are highly likely of actually being so.
Qualitative evaluation To provide qualitative understanding of the retrieval capabilities of our frame- work, we produced matrix reflecting the distance (or similarity) between each pair of patients before retrieving the most similar pair of patients. We present their associated data in Fig, 7
0.82
0.80
0.00
0.76
Pretson
0.77
Lrlyy
-075
074
077
9
10
-02 0.0
0.2
0.4
0.6
0.8
Eacliden Distance Between PCP and Valication Potlents
Cosine Similarty Between PCP and Validation Patients
Figure 6: Patient cardiac prototypes reliably retrieve similar patient data. The similarity (or dissimilarity) metric used is the (left) Euclidean distance or (right) cosine similarity between PCPs and patient data in the validation set. We define the relevance of the retrieved patient data based on whether their corresponding cardiac arrhythmia class matches (or does not match) that of the query PCP. We show that our framework is agnostic to the type of similarity metric (e.g., Fuclidean distance, cosine similarity) used for retrieval.
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Density
0.5
0.0
6
10
12
14
Pairwise Euclidean Distance
similarity
Chapman Patlent Cardlac Prototypes
10.5
Parwoe Euskotann Desance
(n 10 of 6.387)
9.0
7.5
Patient X
Patient
similarity
Chapman Validation Set Patients (n= 10 of 2129)
Patient X from training cohort
Patient from validation cohort
1 M
ground-truth label
ground-trun truth label stnus rhythm
AU
sinus rhythm
Ma
Age
Sex
Ventricular Rate
Atrial Rate
QT Interval
Age
Sex
Ventricular Rate
Atrial Rate
QT Interval
Pemale
84
356
Figure 7: Patient cardiac prototypes retrieve patients who have similar attributes. We show a distribution pairwise Euclidean distances between PCPs and representations in the validation set. We also present a subset of these pairwise distances a matrix reflecting patient- patient distance values. We identify the most similar patient pair (4 Fuclidean distance). and retrieve their corresponding 12-lead electrocardiogram recordings, and where available, additional patient information.
We found that PCPs were able to sufficiently distinguish between unseen patient data and thus act reasonable patient data similarity tools. This is evident by the large range of distance values for any chosen PCP (any matrix row, Fig. 77 In other words, PCPs were closer to some representations than to others, implying that a chosen PCP was not trivially equidistant distinguishing between patient data is sufficient for patient data similarity tool. We found that PCPs also correctly retrieve relevant patient. data. We found that the two patients identified being most similar to another, using method, do indeed share many similarities (Fig 7 bottom). For example, their respective 12-lcad ECG data are both associated with the cardiac arrhythmia label of sinus rhythm. Furthermore, similarities are observed when comparing the cardiac-specific statistics such as ventricular rate (84 in both cases) and atrial rate (84 in both cases). We hypothesize that this behaviour arises due to the ability of PCPs efficiently summarize the cardiac state patient, a finding which reaffirms the potential of PCPs tools for patient data retrieval. We observed similar findings on the remaining datasets (see A Appendix
all other representations. However,
5 Discussion
Our study was inspired by how primary care physicians adapt population-based protocols to the unique patient under thoir care. To that end, we proposed framework which learns emboddings, titled patient cardiac prototypes, that capture the information unique patient's cardiac data. We demonstrated that PCPs, when incorporated into the diagnosis pipeline, allow for the probing of deep learning-based
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medical diagnoses. We also showed that PCPs are effective dataset distillers, where they can be used to train models that match the performance of those trained on full dataset, which is orders of magnitude larger. We also demonstrated that PCPs can reliably retrieve similar patients across clinical databases.
Our framework has the potential to improve the transparency of clinical deep learning systems, providing further insight, into why a deep learning-base medical diagnosis was made for an individual patient. This transparency is likely the deployment of such systems within clinical ecosystems. We believe that these prototypes naturally extend to other disciplines of medicine in which data from a particular cohort can be succinetly summarized in embedding form. Furthermore, as an effective approach to datasct distillation, our framework has the potential to reduce the resources and computational requirements for training clinical deep learning systems. Instead of training models on full datasets, in their raw format, researchers can now share and learn from prototypes directly (which can be orders of magnitude smaller in size than full datasets). Additionally, as clinical data are generated at an ever-growing pace, prototypes allow researchers to search for and quickly retrieve patient data considered relevant for their use-case (e-g.; clinical trial enrolment). For example, recent work demonstrated that prototypes can be learned with additional patient attributes (e.g., sex and age) as a mcans of retrieving and annotating perviously-unlablli cardiac signals (Kiyassch et al. 2021a).
instil clinical stakeholders with trust, and engage them further on the path to
Our study does suffer from several limitations. Despite the multi-purpose use of patient cardiac prototypes, they remain relatively myopic; they do not account for diverse spatial and long-range temporal changes in patient data, When learning PCPs, we implicitly assumed that all representations of cardiac signals belonging to the same patient should be attracted to single PCP. This is a valid assumption if such representations do indeed reflect similar underlying physiological state. However, this assumption may not hold if patient data span multiple years or pertain to distinct modalities (e.g,, imaging, electronic health records, etc.). In such a setting, patient cardiac prototypes would be a spatial and temporal average of a patient's clinical state. Although this summary embedding could be of use in certain scenarios, it might conccal subtle but useful changes in the paticnt's physiological statc.
Although we demonstrated that patient cardiac prototypes have multiple applications, we did observe that a system learned without a contrastive loss (i.e., without prototypes) performed marginally better (AUC 0.90) than one learned with such loss (AUC 0.88). It is unknown whether this difference leads to meaningful and noticeable changes within the clinic. We leave it to future work to explore other variants of contrastive learning (Bardes et al. 2022 More broadly, however, it remains an open question whether there exists a trade-off between system transparency and performance, and in such an event, whether clinical stakeholders are willing to sacrifice some performance in exchange for the improved transparency and widened application areas of deep learning systems.
Moving forward, and as data become available, we aim to learn prototypes over longer time-spans (e.g., years) and from multiple data modalities. Such temporal multi-modal prototypes would allow researchers to (a) monitor changes in the clinical state of patient over time and (b) obtain more holistic summary of that state. For example, single multi-modal prototype might capture the information from coronary angiogram data, an electrocardiogram, and a clinical report all collected on the same day (e.g,, at an annual checkup). A distinct prototype can then be learned for every subsequent anmual checkup, ultimately providing a succinct temporal trajectory of paticnt's 's clinical state. Such prototypes may further propel the diagnostic performance of clinical deep learning systems and inform downstream temporal analyses (e.g.. survival analyses).
We also aim to exploit the quantification of the similarity of patient data to advance graph neural networks. Such networks typically require the design of an adjacency matrix, one that quantifies the presence and weight of edges (relationships) between nodes (patients) (Li et al. 2022). However, designing this matrix is non-trivial, particularly when dealing with physiological data, and can become a computational burden if dense. By interpreting these nodes as patients, our Per-derived similarity values can be used to initialize the weights of edges between nodes and potentially inform the sparsity of node connections. As such, graph neural networks can be thought of as being trained with dcP-derived prior, one that could accelerate, and inject valuable domain knowledge into, the learning process.
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6 Conclusion
Clinical deep learning systems are becoming incrcasingly adept at automating medical diagnoses. However, the vast majority of these systems continue to make predictions that are population-based and opaque. Here, we introduced a framework anchored around the concept of patient cardiac prototypes, learned embeddings which uniquely capture the information of an individual patient's cardiac data. We hope the community joins us in building upon these prototypes in other application areas to improve the trustworthiness of clinical deep learning systems and increase their likelihood of adoption by clinical stakeholders.
Acknowledgements
We thank the anonymous reviewers for their insightful feedback. We also thank Talal Maddah for lending his voice. David Clifton was supported by the EPSRC under Grants EP EP/ PPP09824// and EP /P0207777/111 and by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. Tingting Zhu was supported by the Engineering for Development Research Fellowship provided by the Royal Acadomy of Engincering.
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Appendix
A
Further motivation behind spatial and temporal invariances
We acknowledge that, some cases, intra-patient invariance con be undesirable. An example is when an individual patient's cardiac signals are collected over a large time-span (e.g., on the order of years), during which their physiological state might have changed, and are thus likely to reflect distinct information. By col- lapsing this distinct information into single embedding, as is done with PCPs, relevant insight. From practical perspective, and in our experiments, we avoid this problematic scenario by exclusively considering data points that are on the order of seconds (e.g.. 10 seconds) and which are collected during the same hospital visit (scc Fig. left). To achieve this, we used the patient ID meta information, and, where available, the date of the ECG recording. An intra-patient invariance is therefore likely hold in light of the (a) short time-span over which the data recording took place and (b) low likelihood of an electrocardigram signal exhibiting major morphological changes during this time.
would lose diagnostically
B Datasets
B.1 Data pre-processing
For all of the datasets, frames consisted of 2500 samples and consecutive frames had overlap with one another. Data splits were always performed at the patient-level.
CPSC (Alday et al. 2020). Each ECG recording varied in duration from 6 seconds to 60 seconds with a sampling rate of 500Hz. Each ECG frame in our selup consisted of 2500 samples (5 seconds). We assign multiple labels to each ECG recording as provided by the original authors. These labels are: AF, I-AVB, LBBB, Normal, PAC, PVC, RBBB, STD, and STE. The ECG frames were normalized amplitude between the values of and 1.
Chapman Zheng et al. 2020 Each ECG recording was originally 10 seconds with sampling rate of 500Hz. We downsample the recording to 250Hz and therefore cach ECG frame in our sctup consisted of 2500 samples. We follow the labelling setup suggested by Zheng et al. (2020) which resulted in four classes: Atrial Fibrillation, GSVT, Sudden Bradychardia, Sinus Rhythm. The ECG frames were normalized in amplitude between the values of 0 and
PTB-XL Wagner al. (2020). Each ECG recording was originally 10 seconds with sampling rate of 500Hz. We extract 5-second non-overlapping segments of each recording generating frames of length 2500 samples. We follow the diagnostic class labelling setup suggested by Wagner et al. (2020) which resulted in five classes: Conduction Disturbance (CD), Hypertrophy (HYP), Myocardial Infarction (MI), Normal (NORM), and Ischemic ST-T Changes (STTC). We alter the original setup in two main ways. Firstly, we only consider ECG segments with one label assigned to them. Secondly, we convert the task into a binary classification problem of NORM vs. (CD, HYP, MI, STTC) from above. The ECG frames were normalized in amplitude between the values of 0 and 1.
B.2
Data samples
In this section, we outline the number of instances used during training, validation, and testing for the CPSC, Chapman, and PTB-XL datascts.
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Dataset
Train
Validation
Test
CPSC
157,188 (4,402)
37,296 (1,100)
47,460 (1,375)
Chapman
76,614 (6,387)
25,524 (2,129)
25,558 (2,130)
PTB-XL
286,632 (10,807)
36,816 (1,411)
37,008 (1,383)
Table 2: Number of instances (number of patients) used during training. These represent sample sizes for all 12 leads.
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C Further implementation details
C.1 Network architecture
In this section, we outline the architecture of the neural network used for all experiments.
Layer Number
Layer Components
Kernel Dimension
Conv 1D
7x1x4 (K Cin Cout)
BatchNorm
ReLU
(axPool(2)
Dropout(0.1)
Conv 1D
x 16
BatchNorm
ReLU
(axPool(2)
Dropout(0.1)
Conv 1D
7 16 32
BatchNorm
3
ReLU
MaxPool(2)
Dropout(0.1)
Linear
320 E
4
ReLU
Linear
E x C (classes)
Table 3: Network architecture used for all experiments. K, Cin, and Cout represent the kernel size, number of input channels, and number of output channels, respoctively. A stride of was used for all convolutional layers. represents the dimension of the final representation.
Dataset
Batchsize
Learning Rate
CPSC
256
10t4
Chapman
256
10-4
PTB-XL
256
10t3
Table 4: Batchsize and learning rates used for training with different datasets. The Adam optimizer used for all experiments.
C.2 Baseline methods
In constructing core-sel, the baseline methods (Lucic (Lucic et al. 2016), Lightweight (Bachem el al. 2018)) and Archetypal (Mair & Brefeld 2019h) typically followed a similar strategy. These methods generated a categorical proposal distribution thei weights. For a fair comparison to patient cardiac protolyps (PCPs), we chose k = P where P is the number of PCPs.
all instances in the dataset before sampling ie instances and assigning
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D Description of the ablation studies
To gain a better understanding of the marginal benefit of each of our framework's components on its overall performance, we conducted an extensive set of ablation studies, as outlined next.
D.1 Experimenting with variants of the retrieval mechanism
During inference on an unscen data point, our framework retrieves patient cardiac prototypes learned on exclusively on the training dataset (see Retrieving PCP during inference). We experimented with variants of this retrieval mechanism as part of an ablation study, which we outline next.
Retrieval variant 1 (mean) The first variant, which we refer to as mean, involved taking the average of all PCPs, p Durain -)=1" Pj, regardless of the instance in the held-out set. Since PCPs and the hypemetwork are deterministic during the inference stage, this approach implies that the generated linear parameters are effectively reduced to constant.
The second variant, which we refer to as similarity- weighted mean, involved calculating lincar combination of the PCPs, weighted according to their similarity to the representation, h2 of an instance. Formally, p E/=r s(pj, h,), Py. In effect, this approach exploited information unique to patient's ECG data to down-weight, or up-weight, the contribution of each PCP.
Retrieval variant (similarity-weighhed mean)
Retricval variant 3 (ncarest) The third variant, which refer to nearest, involved the vanilla approach of retrieving the single PCP closest to the representation, h;, In elfect, this approach retrieved the PCP of the patient in the training set that was deemed most similar to the representation of an instance associated with a different patient in the held-out set.
We hypothesized that by restricting the framework to only select single PCP, we were preventing the representation from exploiting potentially useful information contained in idditional PCPs. For example, these additional PCPs could reflect patients with attributes (e.g., sex, age, and treatment outcome) that are shared with, and thus potentially useful for, the patient in the held-out set for whom the prediction is being made. Therefore, our fourth variant of the retrieval mechanism, which we refer to as nearest 10, involved taking the average of the ten PCPs closest the representation, h;.
Retrieval variant 4 (nearest 10)
D.2 Experimenting with variants of the patient cardiac prototypes
To gain some better intuition as to whether PCPs were indeed capturing information unique to patient ECG data, we learned embeddings that differed slightly from our patient cardiac prototypes.
Optimizing the supervised loss alone In this ablation study, we trainod our deep learning framework without contrastive loss term (equation D and, therefore, did not learn the corresponding PCPs. In this scenario, we treated the average representation from each patient (instead of the PCP) the descriptor of individual patient's ECG data. In the Results, we refer to these embeddings Mean Representations.
Optimizing the contrastive loss alone In this ablation study, we exclusively optimized the InfoNCE loss. Here, supervision manifested solely in the form of patient IDs. Importantly, cardiac arrhythmia discase labels are not seen by the network during training. After learning prototypes in this setting, exploited them to perform cardiac arrhythmia classification. In contrast to previous experimental settings, this setting does not include a linear classification head that would have output a probability distribution over the possible classes. Therefore, during inference, we decided to implement the kenearest neighbours (kmn) algorithm as a way to mimic the previously-introde held-out set, we searched for its K nearest prototypes (using cosine similarity) and considered the most frequent cardiac arrhythmia ground-truth label of these prototypes as the diagnosis. To elarify, K = 1 and
retrieval mechanism. Specifically, for each representation in the
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K = 10 can be thought of as being analogous to the nearest and nearest 10 retrieval mechanisms introduced earlier.
E
Further description of the patient data retrieval process
Patient cardiac prototypes have the potential to retrieve similar patient data within clinical databases. To substantiate this claim, we followed these steps.
Step 1 calculate distance between patients When we searched for cardiac signals within dataset, we first calculated the Euclidean distance, d(pj, h,) between the j-th PCP, Py, and the representation, h,, of the i-th instance unseen during training (e.g., those in the validation set). Our motivation for choosing the Euclidean distance, as opposed some other similarity metric, such as cosine similarity, will be discussed later. At this point, we had access to distances between particular patient (in the form of PCP) and representations. However, to obtain distances between patients and other patients, averaged these distance values across representations, h,, of instances associated with the same patient. This process was then repeated for all PCPs. In contrast, when we searched for cardiac signals across distinct datasets, we simply calculated the Euclidean distance between the PCPs of these respective datasets. This immediately provided us with patient-patient distance values.
Step 2 define relevance of retrieved patients Evaluating the relevance of the retrieved patient data is non-trivial. This is because the similarity (and dissimilarity) of patient data from clinical perspective is nebulous. For example, patient data be deemed similar based on attributes such medical history (e.g., cancer survivor), and drug trealment pathways. Unfortunately, publiely-available datasets of cardiac signals do not contain such exhaustive information. However, these datasets do entail cardiac arrhythmia disease labels. Therefore, each pair of patients was assigned a ground-truth relevance score (s = relevant, s = 0 irrelevant) according to whether they shared the samn cardiac arrhythmia label. We then identified pairs of patients as being relevant if their Fuclidean distance, < de, was less than some threshold distance, dg. If, however, we were interested in retrieving dissimilar patients, then we would simply need to redefine the ground-truth relevance score and identify pairs of patients with d dg as being relevant (notice the swap in the sign).
and age,
For the precision metric, we were looking to quantify how many of the retrieved patients (those identified as relovant) were actually relevant. In contrast, for the NPV, we were looking to quantify how many of the retrieved patients (those idintified as irrelevant) were actually irrelevant.
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F Additional results
F.1 Effect of supervision on performance
As part of our ablation studies, we examined the effect of removing the supervision outlined in equation 2 on the overall performance of our framework. We refer to this setup at NCE Loss Only since we only end up optimizing the InfoNCE loss. The prototypes learned in such a manner are similar in spirit to those learned by Li al. (2021) in an unsupervisod manner. We use these prototypes as part of a K-ncarest neighbours model classily cardiac arrhythmias. As apparent in Fig. S we found that these prototypes, which are learned in unsupervised manner, perform worse (AUC 0.60) than those learned with supervision (AUC > 0.75). This is expected the supervision in equation is directly related to the cardiac arrhythmia class. We suspect that other purely unsupervised contrastive learning methods would perform equally poorly.
Only
0.80
0.70
Accurecy
050
0:30
Enbeddinn Dimension
Figure 8: Comparison of the performance of the K-nearest neighbours model with embeddings learned by exclusively optimizing the InfoNCE loss (NCE Loss Only) against that of our framework (Combined Loss). We show that such embeddings are less predictive of cardiac arrhythmia classes than the prototypes learned via our proposed framework.
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F.2 Effect of InfoNCE Loss Dataset Distillation
In this section, we explore the ellect of exclusively oplimizing the InfoNCE loss on dataset distillation. Specifically, we train our network to optimize the InfoNCE loss (without the supervised cross-entropy loss) and learn prototypes an end-to-end manner. To evaluate the datasct distillation capabilities of these learned prototypes, we exploit them to train a support vector machine (SVM) to classify cardiac arrhythmia classes. The intuition that if these prototypes happen be effective dataset distillers, then we would models trained exclusively on them perform equally well (on held-out dataset) as models trained on the entire dataset.
In Fig. g we present the accuracy of the SVM models trained on these prototypes function of the fraction of prototypes available for training, We show that prototypes learned in such manner are ineffective dataset distillers; the performance SVM models trained exclusively on such prototypes is worse than that achieved with model trained on all representations. For example, at F = 1, these two models achieve Accuracy s 0.40 and 0.60, respectivcly. We hypothesize that this poor performance (across all fractions) due to the lack of classdiscciminnttiv behaviour exhibited by the learned protolypes. In other words, there exists a weak mapping from prototypes to cardiac arrhythmia classes. Such finding suggests that, from the perspective of dataset distillation, our proposed combined loss (sec. Methods section of main manuscript) is preferable to the InfoNCE loss.
Traliii Set
0.8
0.5
Accuracy
0.2
0.1
Fraction of PCPs Available
Figure 9: AUC of SVM model trained on fraction, F, of the prototypes and evaluated on held-out set of data. Results are averaged across five random seeds. The horizontal dashed line depicts the performance of model trained on all of the training representations. The prototypes are initially learned via a framework which optimizes only the InfoNCE loss. We show that prototypes learned in such a manner are ineffective dalaset distillers; the performance of SVM models trained exclusively on such prototypes is worse than that achieved with model trained all representations.
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Published in Transactions on Machine Learning Research (01/2023)
F.3 Retrieving similar patients across datasets
In Fig. 10 illustrate the pair of palients (across distinct datasets) identified being most similar to one another based on our PCP framework. We also present the 12-lead electrocardiogram data for this pair of patients in order to help validate our patient retrioval mechanism. We find that PCPs can reliably retrieve patients across distinct datasets that are similar to one another. This is evident by the similar morphology exhibited by the pair of 12-lead ECG data. For example, both patients exhibit normal electrical heart activity, which is also known as normal sinus rhythm.
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Patient Y from PTB-XL dataset
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Figure 10: Exploiting PCPs to discover similar patients across the Chapman and PTB-XL datasets. We show a distribution pairwise Euclidean distances between PCPs and representations in the validation set. We also present a subset these pairwise distances a matrix reflecting patient- patient distance values. We identify the most similar patient pair (1 Euclidean distance) and retrieve their corresponding 12-lead electrocardiogram recordings. We show that PCPs can reliably identify similar patients. This is evident by the high degree of similarity exhibited by the pair of patients. For example, both patients exhibit cardiac arrhyt thmit label of sinus rhythm.
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Published in Transactions on Machine Learning Research (01/2023)
F.4 Retrieving dissimilar patients across datasets
In addition to showing that PCPs can be used to relrieve similar patients, we claim that they can also be exploited for the retrieval of dissimilar patients. In this section, we provide qualitative evidence in support of this claim. In Fig. 11 (top), we present a pair of patients within the same dataset identified as being most dissimilar from one another. In Fig. [1 (bottom), we present a pair of patients across distinct datasets identified as being most dissimilar from one another. In both cases, we find that PCPs can reliably retrieve dissimilar patients. This evident by the observation that the morphology of the pair of the 12-lead ECG data differs. For example, in Fig. 11 (top), the two dissimilar patients exhibit normal rhythm (sinus rhythm) and potentially fatal one (atrial fibrillation).
Dissimilar patients within Chapman dataset
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Patient Y from validation cobort
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sudden bradycardia
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Figure 11: 12-Lead ECG segments corresponding to pair of patients identified as being dis- similar from one another based the PCPs. Dissimilarity is defined high Euclidean distance between patient cardiac prototypes (PCPs) and representations of instances in the validation set. We show that PCPs can reliably retrieve dissimilar patients. This is evident by the observation that the ECG seg- ments between patients exhibit different morphology and correspond to the different cardiac arrhythmia labels (sudden bradycardia vs. ischemic ST-T changes).
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Reviews:
[{"review_id": "dNncPKJE8D", "reviewer": "Reviewer_NeHb", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strength:\n\n- The paper explores several use cases for the trained prototypes in the application of healthcare.\n- The prototypes are useful in the interpretation of the learned representations.\n\nWeakness:\n\n- Missing an important unsupervised prototype-based contrastive learning work [1]\n- Where are the results of experiments mentioned in Section 3.3.2?\n- The paper just skim through dataset distillation and patient retrieval. It's hard to understand what these tasks are, what other baselines do, and why these tasks matter. \n- The claim of novelty in architecture is questionable since it looks quite similar to the attention mechanism. If we view the prototype as word embeddings, a similar architecture is widely used in NLP.\n- It is unclear whether the proposed method can obtain a better classification performance than cross-entropy.\n\n[1] Prototypical Contrastive Learning of Unsupervised Representations. Li et. al. 2020\n\n\n", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper proposes a framework for diagnosing with ECG data. The framework leverages prototype embeddings that are jointly trained with the ECG representation. The paper also shows that the trained prototype can be used for interpreting the network, patient retrieving, and distillation. ", "claims_and_evidence_yn": "No", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "1. Compare with baselines such as CE loss only and unsupervised prototype-based contrastive learning methods\n2. Provide more details about the tasks, baselines, and metrics. \n3. Be cautious about claiming novelty.", "broader_impact_concerns": "There is no ethical concern.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "C3OImbiGky", "reviewer": "Reviewer_FnHS", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n- The paper conducts an extensive set of experiments to evaluate the proposed method in multiple dimensions.\n- If one is interested in learning a PCP, the suggested approach seems like a reasonable one to use.\n\nWeakness:\n- In Figure 4, one can see that PCP actually harms diagnosis accuracy. I suspect this would be even more evident if the paper used a more sensible baseline (e.g. a combination of 1D-Conv and Transformer or the winner of PhysionNet Challenge 2022), rather than simply using the proposed framework (Figure 1) minus the contrastive loss. If you are harming an AI's predictive accuracy to gain a little bit of interpretability, that AI has little chance to be used by real-world users, considering that a bad decision can risk human lives in healthcare.\n- The paper claims that PCP can help physicians during the diagnostic process, because PCP is able to provide insight when the model makes both correct and incorrect decision. But I do not see how this is possible. How can PCP help physicians, when physicians themselves don't have GT labels during the test phase? What if the model makes an incorrect decision but the retrieved PCP looks similar enough to the test sample? Or conversely, what if the model makes a correct decision but the retrieved PCP does not look similar enough to the test sample? \n- It's hard to see why we need prototypes to begin with. If we wanted to develop an interpretable diagnosis system based on retrieving the most similar training sample (although it is unclear whether this actually helps physicians, as mentioned above), we can perform sample-wise representation learning, and use individual samples for retrieval. I do not see why retrieving a patient-level prototype (instead of a single ECG sample) provides any additional value regarding interpretability. Prototypes might be good for dataset distillation, but that is a completely independent topic. \n- The paper is overall very verbose. I see the same content repeatedly described multiple times throughout the paper. One example is how the paper repeatedly describe how they used short time-span ECG samples to ensure intra-patient invariance. The whole material of this paper does not seem to require 16 pages.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper proposes a method to derive patient cardiac prototypes (PCP), which can be used for developing interpretable arrhythmia diagnosis system. In particular, the paper conducts the following experiments using three different ECG datasets to empirically validate the claimed contributions:\n- Ablation study with diverse retrieval methods\n- Ablation study with using only one loss term, instead of two\n- Qualitative example of how PCP enables interpretable diagnosis\n- Dataset distillation experiment\n- Similar/dissimilar patient retrieval experiment\n", "claims_and_evidence_yn": "No", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "The paper needs to rigorously address all weaknesses mentioned above. In my estimation, I see two possibilities:\n- Carefully justify how retrieval-based diagnosis systems can help physicians in the real-world, when the physicians themselves don't even know the true label, and there is no way of knowing if the retrieved sample is indeed the most similar one to the given test sample, all the while the definition of \"similar\" is loosely defined. Then abandon prototypes because sample-wise retrieval can bring the same amount of interpretability as the prototype retrieval. Then demonstrate that sample-wise retrieval does not hurt diagnosis performance compared to none-retrieval-based models. This would naturally lead to abandoning all claims on dataset distillation.\n- Withdraw any claims regarding interpretability. Stick to PCP, but only in the context of dataset distillation.\n\nBut there can always be a more creative solution, and as long as the weaknesses are resolved, I would be happy to re-evaluate this work.", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "q7278iWzvz", "reviewer": "Reviewer_9duA", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n\n— the experimental setup is appropriate\n— the ablation studies are informative, including visualisations\n— overall the presentation is fine, but some typos exist, e.g. in contribution number 4.\n\nWeaknesses\n\n— although the presentation is fine, the writing up could be improved in that one could flesh out what the technical contributions of this paper are. InfoNCE loss is not cited when it is first used, so I think the 2018 paper that proposed it (contrastive Predictive Coding; van den Oord, et al. 2018). \n— The intermediate embeddings are not different to any other embeddings one can develop with raw data, but it is not clear to me how the contrastive component helps to develop the PCPs in a way that one can trace the outcome back to the single PCP based on some Euclidean metric - more explanation would be useful\n— I am not a clinician therefore my opinion should be taken with a pinch of salt, but the second sentence in the abstract might not be accurate. In certain countries, GPs or other physicians do follow population-based protocols to make decisions for individuals, albeit sometimes adapting to individual circumstances. However it is not always individualised, hence precision medicine is an area that people are looking towards for the future.\n", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "Firstly, I would like to highlight that the paper gets better as it progresses through to the experimental section. Before that the paper is a bit shallow in that it should be more concise. Secondly the paper provides a framework to distill information from raw data into something they term patient cardiac prototypes (PCP), given they are using ECGs. They argue that their framework, that is based upon contrastive learning, can generate embeddings (i.e. PCPs) that can replace the raw data (i.e. ECGs) which are more volume/size demanding. The overarching aim is to develop a more interpretable way of doing inference, hence being able to refer back to the original PCP that contributed to the outcome. In addition, the process involves the development of a hyper network that gets an input representation from there feature extractor which then provides an input to the linear classification layer. Lastly the authors provide a set of experiments to show how PCPs can be used to detect cardiac arrythmia, as well as a set of ablation studies to show how the proposed framework contributes to the overall performance and interpretability in the diagnosis.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "— To my understanding, at the inference stage, the unseen input is associated with a specific PCP but at the same time it lends characteristics from the wider representation space. Is that accurate? If so, I am curious as to how well this generalises in cases beyond the PCPs proposed here, i.e. whether that is observed in other cases as well. Are figures 6, 7 supposed to illustrate this fact?\n\n— The literature review is rather brief, e.g. what studies exist in this area, and also what other self-supervised learning approaches could be relevant, i.e. why contrastive learning here?\n\n— Discussion could be better organised to provide a section for conclusion.", "broader_impact_concerns": "Not an ethical concern per we, but it could be that intepretability arguments with respect to medical diagnosis might be better tackle with the involvement of clinicians and/or additional experiments.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)