PCPs: Patient Cardiac Prototypes
Authors: Dani Kiyasseh
Tingting Zhu
David A. Clifton
Venue:  (2021)
Keywords: Contrastive learning
dataset distillation
patient-similarity
physiological signals
healthcare

Abstract: Existing deep learning methodologies within the medical domain are typically population-based and difficult to interpret. This limits their clinical utility as population-based findings may not generalize to the individual patient. To overcome these obstacles, we propose to learn patient-specific representations, entitled patient cardiac prototypes (PCPs), that efficiently summarize the cardiac state of a patient. We show that PCPs, learned in an end-to-end manner via contrastive learning, allow for the discovery of similar patients both within and across datasets, and can be exploited for dataset distillation as a compact substitute for the original dataset.

Under review as a conference paper at ICLR 2021
PCPs: PATIENT CARDIAC PROTOTYPES
Anonymous authors
Paper under double-blind review
ABSTRACT
Many clinical deep learning algorithms are population based and difficult inter- pret. Such properties limit their clinical utility as population-basee findings may not generalize to individual patients and physicians are reluctant incorporate opaque models into their clinical workflow. To overcome these obstacles, we propose to learn patient-specific embeddings, entitled patient cardiac prototypes (PCPs), that efficiently summarize the cardiac state of the patient. To do so, we attract representations of multiple cardiac signals from the same patient to the corresponding PCP via supervised contrastive learning. We show that the utility of PCPs is multifold. First, they allow for the discovery of similar patients both within and across datasets. Second, such similarity can be leveraged in conjunction with a hypernetwork to generate patient-specific parameters, and in turn, patient-specific diagnoses. Third, we find that PCPs act compact substitute for the original dataset, allowing for dataset distillation.
INTRODUCTION
Modern medical research is arguably anchored around the "gold standard" of evidence provided by randomized control trials (RCTs) (Cartwright 2007). However, RCT-derived conclusions are population-basee and fail to capture nuances at the individual patient level (Akobeng 2005). This is primarily due to the complex mosaic that characterizes a patient from demographics, to physiological state, and treatment outcomes. Similarly, despite the success of deep learning algorithms in automating clinical diagnoses (Galloway et al., 2019 Áttia al., 2019a by Ko et al. 2020). network- generatdd predictions remain population-based and difficult to interpret. Such properties are a consequence of a network's failure to incorporate patient-specifie structure during training inference. As result, physicians are reluctant to integrate such systems into their clinical workflow. In contrast to such reluctance, personalized medicine, the ability deliver the right treatment to the right patient the right time, is increasingly viewed as a critical component of medical diagnosis (Hamburg & Collins 2010).
The medical diagnosis of cardiac signals such as the electrocardiogram (ECG) is of utmost importance in a clinical setting (Strouse et al. 1939). For example, such signals, which convey information about potential abnormalities in a patent's 's heart, also known as cardiac arrhythmias, are used to guide medical treatment both within and beyond the cardiovascular department (Carter 1950). In this paper, conceptually borrow insight from the field of personalized medicine in order to learn patient representations which allow for a high level of network interpretability. Such representations have several potential clinical applications. First, they allow clinicians to quantify the similarity of patients. By doing so, network- network-generated predictions for a pair of patients can be traced back to this similarity, and in turn, their corresponding ECG recordings. Allowing for this inspection of ECG recordings aligns well with the existing clinical workflow. An additional application of patient similarity is the exploration of previously unidentified patient relationships, those which may lead to the discovery of novel patient sub-cohorts. Such discoveries can lend insight into particular diseases and appropriate medical treatments. In contrast to existing patient representation learning methods (Zhu al. 2016) Suo et al. 2017). we concurrently optimize for predictive task (cardiac arrhythmia classification), leverage patient similarity, and design a system specifically for 12-lead ECG signals.
Contributions. Our contributions are the following:
1. Patient cardiac prototypes (PCPs) we learn representations that efficiently summarize the cardiac state of a patient in an end-to-end manner via contrastive learning.

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2. Patient similarity quantification show that, by measuring the Euclidean distance between PCPs and representations, we can identify similar patients across different datasets.
3. Dataset distillation we show that PCPs can be used to train a network, in lieu of the original dataset, and maintain strong generalization performance.
RELATED WORK
Contrastive learning a self-supervised method that encourages representations of instances with commonalities to be similar to one another. This is performed for each instance and its perturbed counterpart (Oord et al., 2018: [hen et al.] 2020a bj Grill et al.] (020) and for different visual modalities (views) of the same instance (Tian et al. 2019). Such approaches are overly-reliant on the choice of perturbations and necessitate a large number of comparisons. Instead, Caron et al. (2020) propose learn cluster prototypes. Most similar to our work is that of Cfeng al. (2020) and CLOCS representations
(Kiyasseh et al. 2020) which both show the benefit of encouraging to be similar to another. Although DROPS (Anonymous) 2021) leverages contrastive learning, does so at the patient-attribute level. In contrast to existing methods, we learn patient-specific representations, PCPs, in an end-to-end manner
Meta-lcarning designs learning paradigms that allow for the fast adaptation of networks. Prototypical Networks (Snell et al. 2017) average representations to obtain class-specific prototypes. During inference, the similarity of representations to these prototypes determines the classification. Relational Networks (Sung al., 2018) build on this idea by learning the similarity of representations to prototypes through a parametric function. Gidaris & Komodakis (2018) and Qiao et al. (2018) exploit hypernetworks (Ha et af. 2016) and propose to generate the parameters of the final linear layer of a network for few-shot learning on visual tasks. In contrast, during inference only, we compute the cosine similarity between representations and PCPs and use the latter as the input to a hypernetwork.
Patient similarity aims at discovering relationships between patient data (Sharafoddini et al. (017)) To quantify these relationships. Pai Bader (2018) and (Pai et al., 2019) propose Patient Similarity Networks for cancer survival classification. Exploiting electromic health record data, Zhu et al. (2016) use Word2Vec to learn patient representations, and Suo et al. (2017) propose to exploit patient similarity to guide the re-training of models, an approach which is computationally expensive. Instead, our work naturally learns PCPs as efficient descriptors of the cardiac state a patient.
3 METHODS
3.1 LEARNING PATIENT CARDIAC PROTOTYPES VIA CONTRASTIVE LEARNING
We assume the presence of dataset, D= (I,, y1 J comprising N ECG recordings, 2, and cardiac arrhythmia labels, y. for a total of Ptot patients. Typically, multiple recordings are associated with single patient, This could be due to multiple recordings within the same hospital visit or multiple visits to a hospital Therefore, each patient is associated with N/Ptot recordings. We learn a feature extractor fe x E IR_ + h E E parameterized by 0, that maps a D-dimensional recording, æ, an E-dimensional representation, h. In the quest to learn patient-specifie representations. we associate each patient, p. out of a total of P patients in the training set with unique and learnable embedding, V E IR F. in set of embeddings, V, where [V| = P N. Such embeddings are designed to be efficient descriptors of the cardiac state of a patient, and we thus refer to them as patient cardiac prototypes or PCPs.
We propose to learn PCPs in an end-to-end manner via contrastive learning. More specifically, given an instance, I₁, that belongs to particular patient, k, we encourage its representation, h4 = fo(x.), to be similar to the same patient's PCP, Uk. and dissimilar to the remaining PCPs, V, / k. We quantify this similarity, s(,,, vx), by using the cosine similarity with a temperature parameter, т. The intuition is that each PCP, in being attracted to diverse set of representations that belong to the same patient, should become invariant to insidious intra- aattent differences. For a mini-batel size, B, the contrastive loss is as follows.
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B Σ log
(hamm))
fo(x;) -g (,,(
Ceontrastive
(1)
s(he,
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3.2 GENERATING PATIENT-SPECIFE PARAMETERS VIA HYPERNETWORKS
Network parameters typically updated during training and fixed during inference. This allows the parameters to exploit population-based information in order to learn high-level features useful for solving the task at hand. Such approach, however, means that all instances exposed the same set of parameters during inference, regardless of instance-specifie information. Such information can be related to any meta-label including. but not limited to, patient ID, geographical location. and even temporal period. As an exemplar, and motivated by the desire to generate patient-specine diagnoses, we focus on patient-specifie information. We are essentially converting traditional classification task to one that is conditioned on sptient-specific information. To perform such conditioning, we propose to exploit both PCPs and hypernetworks, as explained next.
We assume the presence of hypernetwork, d6 h IR + w E R parameterized by é, that maps an E-dimensional representation, h, to a matrix classification parameters, w. where C' is the number of class labels. During training, we feed representation. h4 to the hypernetwork and generate instance-specifie parameters, Wi (sce Fig. Ieftt) During inference, however, we retrieve, and feed into the hypernetwork, the most similar PCP, Uk. to the current representation, h, (based on similarity metric, 8). We chose this strategy after having experimented with several of them (see Sec. Sec.5.2).. It is worthwhile to note that although this approach bears some resemblance to clustering, it is distinct from it. In clustering scenario, we would have assigned labels to instances based on leveraging this proximity determine the input of
their proximity PCPs, In contrast,
hypernetwork (see Fig. right).
ge
for training
W
9 (1xk)) for inference, 2 = arg max s(h;, U3)
(3)
%
By performing this retrieval, we exploit the similarity between patients in the training and inference set. As a result, the hypernetwork generates patient-specific parameters that parameterize the linear classifier, Pw : RR* y RC, which maps representation, h. posterior class distribution, We train the entire network in an end-to-end manner using a combined contrastive and supervised loss.
Esupervised
Σ
log Pw; clh,) (4) Lcombined Leontrastive + Lsupervised (5)
Training Stage
Inference Stage
Nature Eeraeor
Linsar Dasdtte
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fa (hlj))
h
Pl. (r(h))
fo (h(x))
Pla (y' (h)
Leontrantive
Lapperutsed
@
@
Ret
(woli)
Figure Training and inference stages of the personalized diagnosis pipeline. During training, instance representations, h, have dual role. They are fed into hypernetwork, Go. to generate parameters for linear classification layer, Pw, that outputs prediction, ÿ. They are also encouraged to be similar to their corresponding patient cardiac prototype (PCP), During inference, the nearest PCP, Ug, to the representation is fed into the hypernetwork, thus generating sppecfi- parameters for classification.
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4 EXPERIMENTAL DESIGN
4.1 DATASETS
We conduct experiments using PyTorch (Paszke et al., 2019) on three large-scale ECG datasets that contain significant number of patients. PhysioNet 2020 ECG consists of 12-lead ECG recordings from 6,877 patients alongside labels corresponding to 9 different classes cardiac arrhythmia. Each recording be associated with multiple labels. Chapman ECG (Zheng et al. 2020) consists of 12-lead ECG recordings from 10,646 patients alongside labels corresponding to different classes of cardiac arrhythmia. As is suggested by /Zheng al. (2020), we group these labels into 4 major classes. PTB-XI ECG (Wagner et al. (020) consists of 12-lead ECG recordings from 18,885 patients alongside 71 different types of annotations provided by two cardiologists. We follow the training and evaluation protocol presented by Strodthofff et al. (2020) where we leverage the diagnostic class labels. We alter the original setup only consider ECG segments with one label assigned to them and convert the task into binary classification problem. Further details can be found in Appendix A.I
Unless otherwise mentioned, datasets were split into training, validation, and test sets according to patient using 60, 20, 20 configuration. In other words, patients appeared in only one of the sets. Further details about the dataset splits can be found in Appendix A.2
4.2 HYPERPARAMETERS
When calculating the contrastive loss, we chose = 0.1 as in Kiyassch et al. (2020). We also use the same neural network architecture for all experiments. Further implementation details can found in Appendix B
EXPERIMENTAL RESULTS
5.1 PATIENT CARDIAC PROTOTYPES ARE DISCRIMINATIVE
During training. we optimize an objective function that consists of both a supervised and contrastive loss term (see Eq. (5). Based on the former, we expect representations to exhibit discriminative behaviour for the task at hand. The latter term encourages these representations to be similar to PCPs, and thus we also expect PCPs to be discriminative. In Fig. 2 instances in the training set and the PCPs after being projected to 2-dimensional subspace using t-SNE and colour-coded according to their class label. dass-discriminative. This can be seen by the high separability of the projected
we illustrate the representations of
find that both training representations,
h, and PCPs, representations along class boundaries, Based on this finding alone, one could make the argument that PCPs are trivially detecting class label differences between patients.
AFIB
GSVT
SB
SR
(a) Training representations
(b) PCPs
Figure 2: t-SNE projection of (a) representations, h R228 of instances in the training set of the Chapman dataset and (b) PCPs, e 128, learned on the training set, colour-coded according to the arrhythmia label assigned to each patient. Learned PCPs are also class-discrmminatve..
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5.2 EFFECT OF HYPERNETWORK INPUT STRATEGY ON PERFORMANCE
As described, our pipeline uses the PCP nearest to each representation as input to the hypernetwork. This approach places a substantial dependency on that single chosen PCP. Therefore, we explore three additional input strategies that incorporate PCPs differently. Nearest 10 searches for, and takes the average of. the 10 PCPs that are nearest to the representation. Mean simply takes the average of all PCPs. Similarity-Weighher Mean takes linear combination of all PCPs, weighted according to their cosine similarity to the representation. Fig. 3 test set AUC as the embedding dimension, E, is changed.
we show the effect of these strategies on the
We find that exploiting the similarity of repre- sentations during inference benefits the gener- alization performance of the network. This shown by the inferiority of the mean strategy relative to the remaining strategies. For exam- ple, at E 256, the Mean strategy achieves an AUC x 0.50, equivalent to a random guess. However, simply weighting those PCPs according to their similarity to representations, as exempli- fied by Similarily Weighted Mean achieves an AUC z 0.65. This implies that representations are capturing patient-speciff information.
Notrest
WWeightdd
0.7
0.4
32
256
Embedding Dimension
We also find that it is more advantageous to ex- ploit similarity to identify the nearest PCPs than to weight many PCPs. In Fig. 3] the Nearest and Nearest. 10 input strategies perform best, with the latter achieving AUC s 0.90, regardless of the embedding dimension. We hypothesize that such behaviour be attributed to the notion that fewer PCPs are less likely to overwhelm the hy- pernetwork. This, in turn, allows the hypernetwork linear classification layer. Moreover, the strong performance of these strategies despite their high dependence on so few PCPs reaffirms the utility of the learned representations.
Figure 3: AUC test set of Chapman dataset as a function of hypernetwork input strategies and embedding dimension, E. Bars averaged across five seeds and the error bars illustrate one standard deviation. The Nearest 10 input strategy outperforms its counterparts and is unaf- fected by changes to the embedding dimension.
generate reasonable parameters for the
5.3 PATIENT CARDIAC PROTOTYPES ARE PATIENT-SPECIPG
So far, we have shown that PCPs are dlass-discriminatio and can assist in achieving strong general- ization performance. In this section, we aim to validate our initial claim that PCPs are patient-specific. To do so, calculate the Euclidean distance between each PCP and two sets of representations. The first includes representations corresponding to the same patient as that of the PCP (PCP to Same Training Patient). The second includes representations that correspond all remaining patients (PCP to Different Training Patients). In Fig. 4
we illustrate the distribution of these distances.
We find that PCPs are indeed patient-specific. This can be between PCPs and representations of the same patient (R 4.5) than between PCPs and representations of different patients (+e 9.5). Such a finding implies that PCPs are, on average, a factor of two more similar to representations from the same patient than they are to those from other patients.
by the smaller average distance
We also set validation set (as is done in Fig. [ was appropriate. In Fig. 4 we overlay the distribution of distances between the PCPs and representations of instances from the validation set (PCP to Validation Patients). We find that comparing PCPs to representations of instances in the validation set is appropriate. This is emphasized by how the average Fuclidean distance between these two entities (~ 9) is on the same order of the average Euclidean distance between PCPs and representations of instances the training set (+= 4). Based on the distributions in Fig. 4 we can also confirm that patients in the validation set are not present in the training set, as was enforced by design. This can be seen by the minimal overlap between the blue and purple distributions. Such a finding suggests that PCPs can be deployed to detect out-of df-iirribbutio patients or distribution shift. We also illustrate the generalizability of these findings by arriving at the same conclusion on the PTB-XL and PhysioNe:2020 datasets (see Appendix
investigate whether computing their similarity representations of instances in the
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1.75
1.50
Patlents
1.25
10
Densiey
1.00
Dersity
0.75
10P
0.50
10"4
0.25
0.00
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0.5 0.8 10
Pairwise Euclideen Distance
Painwise Cosine Distance
Figure 4: Distribution of pairwise (left) Euclidean and (right) cosine distance from the learned PCPs on the Chapman dataset to three sets representations: those in the training set that belong to the same patient (blue), those in the training set that belong to different patients (orange), and those in the validation set (purple). PCPs are patient-specific since they are closer to representations belonging to the same patient than they are representations belonging to different patients.
5.4 DISCOVERY OF SIMILAR PATIENTS VIA PATIENT CARDIAC PROTOTYPES
Having established that PCPs are patient-specific and classddscriminntive,, now investigate whether they can be exploited to quantify patient similarity. From a clinical perspective, such information allow physicians to discover similar patient sub-cohorts and guide medical treatment. This is particularly consequential when patients are located across different healthcare institutions. Patient similarity quantification also has the potential to add layer of interpretability exigent network- generated diagnoses. In this section, we exploit, and validate the ability of, PCPs to identify similar (and dissimilar) patients.
To quantify patient similarity, we compute the pairwise distance (e.g.. Euclidean) between each PCP and each representation in the validation set. The distribution of these distances can be found in Fig. 3 We average these distances across representations that belong to the same patient, and generate a matrix of distances between pairs of patients (see Fig. centre for subset of that matrix). Validating our findings, however, is non-trivial since similarity can be based on patient demographics. physiology, or treatment outcomes. With this in mind, we decide to validate our findings both qualitatively and quantitatively. For the former, we locate the cell in the distance matrix with the lowest distance, and in turn, identify the most similar pair patients. We then visualize their corresponding 12-lead ECG recordings (Fig. 5 bottomm).
We find that PCPs are able to sufficiently distinguish between unseen patients and thus act as reasonable patient-similari tools. In Fig. 5 (centre),
that there exists large range of distance values for any chosen PCP (row). In other words, it is closer to some representations than to others, implying that chosen PCP is not trivially equidistant to all other representations. However, distinguishing between patients not sufficient for a patient-similarity tool. We show that PCPs can also correctly capture the relative similarity these patients. In Fig. 5 (bottom), two patients identified as being most similar to one another, using our method, have ECG recordings with similar morphology and arrhythmia label, supra-ventricular tachycardia. We hypothesize that this behaviour arises due to the ability of PCPs to efficiently summarize the cardiac state of a patient. Such finding reaffirms the potential of PCPs as vatient-similart tools. We also repeat the above procedure in attempt to discover similar and dissimilar patients across different datasets. In doing so, we arrive at positive results and similar conclusions to those above (see Appendix D).
show that the
We now move on to quantitatively validate the PCP-derived patient similarity values. Conceptually. we build on our qualitative findings and assume that a pair patients, identified as being similar by our method, are in fact similar if they happen be associated with the same cardiac arrhythmia label. More specifically, we retrieve all pairs of patients that are more similar than some threshold distance, dg, and determine what percentage of such retrieved pairs consist of patients with matching cardiac arrhythmia label (Precision). In Fig. we illustrate this precision as a function of different threshold distances.

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0.5
Density
0.0
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6
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12
Pairwise Euclidean Distance
Chapman Patient Cardiac Prototypes
10.5
Pairwion Eudidean Distanoe
(n 10 of 6.387)
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Chapman Validation Set Patients (n of 2129)
Patient from training cohort
Patient from validation cohort
u
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Figure 5: (Top) Distribution of all pairwise Euclidean distances between PCPs and representations in the validation set. (Centre) Matrix illustrating average pairwise distances between a subset of PCPs and representations of patients in the validation recordings of the two patients identified as being most similar by our method. Both recordings are similar and correspond the same arrhythmii, supra- veatrrvent tachycardia, thus lending support to PCPs as reliable patient-similarity tool.
(Bottom) Visualization of the 12-lead ECG
We find that PCP-derived similarity values are able to identify patients with matching cardiac arrhyth- mia labels. For example, 90% of the pairs of pa- tients that are deemed very similar to one another (i.e., dg < 6.0) exhibit perfect cardiac arrhyth- mia label match. As we increase the threshold distance, dE 8.5, we see that Precision - 0. Such a decay is expected of a reasonable similarity metric where patients that are deemed dissimilar do not match according to their cardiac arrhythmia labels. Moreover, based on an acceptable level of precision, (e.g., 0.90), we can identify an appro- priate threshold distance (e.g., dg &s 6.2). worthwhile to note that this specific threshold co- incides with the region of minimal distribution overlap observed in Fig. 4 This suggests that a simple threshold can be derived from those dis- tributions.
0.
0.8
4. Fuckitan Distance Reiteeen PCP and Vill diation Patsents
5.0
7.0
7.5
8.0
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Figure 6: Percentage of pairs of patients, re- trieved based on their PCP-derived similarity, with matching cardiac arrhythmia label. Based on a desired level of precision (e.g., 0.90), we can identify appropriate threshold distance between patients (e.g., dR Rs 6.2).
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5.5 DATASET DISTILLATION VIA PATIENT CARDIAC PROTOTYPES
Our interpretation and the growing evidence we have presented in support of PCPs efficient descriptors of the cardiac state of a patient led us to investigate the following question: could PCPs be sufficient for training classificatiin tasks, in lieu of the original dataset? This idea is analogous to dataset distillation which focuses on obtaining a coreset of instances that do not compromise the generalization capabilities of model (Feldman et al. 2018 Wang et al. 2018).
To investigate the role of PCPs as dataset distillers, we train Support Vector Machine (SVM) on them and evaluate the model representations of held-out instances. We compare PCPs three coreset construction methods: 1) Lucic (Lucic et al., (016)). 2) Lightweight (Bachem et al. 2018). and 3) Archetypal (Mair& Brefeld 2019). In constructing the coreset, these methods generate categorical proposal distribution over all instances in the dataset before sampling k instances and assigning them weights. For fair comparison to our method, we chose k where P the number of PCPs. In addition to exploring the effect of these coreset construction methods based on raw instances, we do so based on representations of instances learned via our network. Table we illustrate the performance of these methods various datasets.
Ful
Coresct
Chapman
PhysioNet2020
PTB-XL
Instances
Lucic
5688
50.1
0.88
Lightweight
56.6 + 0.4
50.1 = 0.1
Archetypal
54.8 + 0.3
50.1 0.1
0.84
Representations
Lacie
57,8 17.5
50.6 1.2
Lightweight
58.9 + 16.8
50.5 1.2
52.4
0.80
Archetypal PCPs
58.1 +: 16.8
50.5 1.2
51.0 1 5.0
88.7 1..7...
528 0.1
63.5 0.7
0.78 0.0b
0.3
0.2 Frazton of PCPs Available
0.5
3.0
Table 1: Validation AUC as a function of the coreset strategy. We use SVM for the Chap- man and PTB-XL datasets, and a Random Forest for the PhysioNet2020 dataset given its multi- label classification formulation. In all experi- ments, the coreset size is equal to the number of PCPs. Results are average across five seeds.
Table 2: Validation AUC after training an SVM on a different fraction of available PCPs (E = 32). By training on 100% of PCPs (N 6387), we achieve similar performance to when training on the full training set (N 76644), illustrat- ing the dataset distillation capabilities of PCPs. Results are shown across 5 seeds.
In Table I we find that coresets of raw instances generated by traditional coreset construction methods are insufficient for achieving strong generalization performance. For example, the A Archetypal method achieves an AUC 34.8 on Chapman. Such poor performance is likely attributed to the poor class separability of the input features. Nonetheless, we show that the exact same set of methods still perform poorly, albeit slightly better, even when constructing coresets from network-derived representations that have been shown to be separable (see Fig. 2a). For example, the A Archetypal method now achieves an AUC 58.1 on Chapman. In contrast, we show that PCPs are relatively more effective coresets than those constructed by the remaining methods. On Chapman, for example, PCPs achieve an AUC 88.7. 7. These findings suggest that PCPs have the potential effectively summarize datasets in compact manner and act as dataset distillation tools.
Having shown the utility of PCPs as dataset distillers, we wanted to investigate the extent which further distillation was possible. In Fig. 2 we illustrate the generalization performance of models trained on a different fraction of the available PCPs. For comparison's 's sake, we also show the AUC of our network when trained on all instances in the training set (Full Training Set), which is several folds larger than the number of PCPs. We find that PCPs do indeed act as effective dataset distillers. In Fig. 2 we show that training on 100% of the PCPs (N 6, 387) achieves
AUC as 0.89 which is similar to that achieved when training on the full training set (N = 76, 614). In other words, achieve similar generalization performance despite 12-fold decrease in the number of training instances. We also show that further reducing the number of PCPs, by selecting a random subset for training, does not significantly hurt performance. For example, training with only 5% of
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available PCPs (N 319) achieves AUC z 0.82. Concisely, this corresponds to 7% reduction in performance despite a 240-fold decrease in the number of training instances relative to that found in the standard training procedure. We arrive at similar conclusions when changing the embedding dimension, E (see Appendix [F). Such a finding supports the potential of PCPs at dataset destillers. We hypothesize that this behaviour arises due our patient-centric contrastive learning approach. By encouraging each PCP to be similar to several representations of instances that belong to the same patient, able to capture the most pertinent information and shed that which is least useful.
5.6 DISCUSSION AND FUTURE WORK
In this paper, we proposed to learn efficient representations of the cardiac state of a patient, entitled patient cardiac prototypes, using a combination of contrastive and supervised learning. We showed that patient cardiac prototypes are both patient-specife and discriminative for the task at hand. We successfully deployed PCPs for the discovery of similar patients within the same dataset and across different datasets. This opens the door to leveraging clinical information that is available in disparate healthcare institutions. Lastly, we illustrated the potential PCPs as dataset distillers, where they can be used to train models in lieu of larger datasets without compromising generalization performance. We now elucidate several future avenues worth exploring.
Obtaining multi-modal summary of cardiac state of patient. Although our approach vali- dated on multiple, large, time-series datasets, it was limited to a single modality, the ECG. Incorporat- ing additional modalities to our approach such as coronary angiograms, cardiac MRI, and cardiac CT, may provide a more reliable summary of the cardiac state of the patient. This could ultimately lead to more reliable patient similarity quantification.
Guiding design of graph neural networks. Arriving at ground-truth values for the similarity of a pair of patients is non-trivial. Recently, graph neural networks have been relatively successful at discovering and quantifying the similarity of instances, but most necessitate a pre-defined graph structure, which may be difficult to design in the context of physiological signals. We believe that designing this graph structure can be facilitated by our patient-similarity scores, for instance, by using them as an initialization of the edge weights between nodes.
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11


Reviews:
[{"review_id": "uUQX7E42qc3", "reviewer": "AnonReviewer4", "summary": "", "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": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "Intuitive concept and interesting study.\n\nSome general comments. \n\nIt would be helpful to know why authors used cosine similarity score as opposed to other metric. Just because of its simplicity? explain.\n\nWhat is the difference between this work and a clustering approach, this seems to be somehow a clustering solution they are providing. I suggest having a comparison with existing phenotypic (clustering) that have been proposed and show how this new model is better (if) and if such comparison is unreasonable, then I except to have a clear justification on why and how in theory this is different and perhaps better. \n\nThe motivation can be further emphasized. I understand the rational given my experience working in healthcare setting; however, other readers may not see the true value of this project as presented. \n\nThe English is at time very assertive and strong use of statement, where instead the patten of distributions are not confirming but rather corroborating since the experiments are not systemic but rather authors show few distributions that seem to demonstrate what they want to highlight. I do not say, the authors are cherry picking but the way it is presented can be misleading.   \n\nIn terms of manuscript, the abstract is too short and should have the typical sections for the reader to get a sense before jumping to the full manuscript. Furthermore, as abstracts are usually used in automated searches and meta-analysis, is highly important to have a clear and comprehensive abstract (background, method, results, discussion and conclusion). \n\nCode should be made available for this submission. ", "extra_scores": {}, "extra_text": {}}, {"review_id": "_jEaBoYqM1I", "reviewer": "AnonReviewer1", "summary": "", "questions": "", "limitations": "", "rating": 5, "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": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "ICLR PAPER \n\nPCPs: Patient Cardiac Prototypes\n\n\n##########################################################################\n\nSummary:\n\n \nThe paper proposes unsupervised neural network models to learn patient-specific representations for ECG applications. The architecture implements ideas from prototype networks and contrastive learning in order to discover similar patients across datasets and compress datasets based on the representations only. \n\n##########################################################################\n\nReasons for score: \n\n \nOverall, I vote for marginal reject. The paper is very well written and I like the idea of leveraging the representations of ECG data but my major concerns are the difference in terms of novelty when compared to other concurrent papers submitted to this venue, namely DROPS, CLOPS (arxiv, Kiyasseh et al.), and CLOCS (arxiv, Kiyasseh et al.). They all seem to use the same datasets and building upon the contrastive learning paradigm. The tasks might be slightly different however all share significant common ground.\n\n \n##########################################################################Pros: \n\n \n1. The paper leverages one of the most important and overlooked data modalities, that of ECG. The community should invest more in models tailored to this kind of sensor data.\n\n2. This paper provides comprehensive experiments, including both qualitative analysis and quantitative results, to show the effectiveness of the proposed framework. I especially enjoyed the euclidean distance plots.\n\n \n##########################################################################\n\nCons: \n\n \n1. My major concern, as described above is the novelty compared to other methods proposed recently, possibly by the same team. A comprehensive comparison/ablation across all these methods is needed in order to evaluate what achieves better results in which task.\n \n2. Also, I struggle to understand if the resulting PCPs are fewer data points than the original training set. As far I understand, a patient generates a very long time series of ECG which has to be segmented to chunks or windows. Now, PCPs compress all these windows to a single latent vector (?). In this regard, why do we need the fancy hypernetwork? Could we just average all latent representations of the same patient after training is done? It seems that the paper is using a \"mean\" baseline but this is still part of the PCPs, not all training samples. \n \n3. In terms of tasks, the paper presents the patient similarity and the dataset distillation as the most important applications of this method. However there are no comparisons with any other methods, for example, established methods from distillation (teacher-student, core-sets). \n\n \n##########################################################################\n\nQuestions during the rebuttal period: \n\n \nPlease acknowledge all concurrent papers using similar datasets/methods/tasks.\n\nThe hypernetwork module is not motivated sufficiently.\n\nAre there other tasks that this method could be useful to?\n\n", "extra_scores": {}, "extra_text": {}}, {"review_id": "Mf8dfe66T3s", "reviewer": "AnonReviewer3", "summary": "", "questions": "", "limitations": "", "rating": 2, "confidence": 5, "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": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "This paper proposes to learn patient-specific representation using patient physiological signals.  The authors design a PCP representation for each patient, which is learned to agree with signals from the same patients and disagrees with the remaining patients. In the supervised part, the classifier is generated from patient-specific parameters by meta-learning. The model was evaluated on three large ECG datasets: PhysioNet 2020 ECG, Chapman ECG, PTB-XL ECG.\n\nStrength: \nThis is an important problem\nThe paper is easy to follow, and the experiment settings are well elaborated \n\n___________________________________________________________________________\nWeakness:\n\nThe authors are ignorant to several lines of existing literature in deep learning for healthcare, particularly deep phenotyping and deep patient subtyping. They also seem not aware of the existing works on explaining Black-box models.   For deep phenotyping algorithms, the patient representations generated from existing deep phenotyping algorithms are ALL patient specific.  To list a few, the authors may take a look at [1-4]. For more related works on deep phenotyping, you may refer to [5]. For more related works on deep learning for ECG data, you may refer to [6]. To learn disease subtypes, patient prototypes, there are also a series of works. Below are several the authors could refer to [7-10]. In addition, there are several model agnostic algorithms designed for explaining deep learning models that could be used here to add more explanability. For example [11-13]. In addition, there are many existing works around contrastive learning on ECG signals, see [14-16]. The authors are either not aware of these lines of works or have wrong understanding on them, which cause several major issues as listed below.\n\n(a) Lack of Novelty. Given the existing works listed below. The novelty of the paper is not enough for the conference. The methodology of the paper consists of a standard contrastive learning method and a standard meta-learning setting. All have been done in one or more existing works.\n\n(b) Lack of Baselines. The paper does not include any baseline. It is unclear how it compares with existing works.  \n\n(c) The experiment results are mostly unconvincing. For example,\n- In Experiment 5.1, the paper states that “PCPs exhibit tighter clusters than those found with training representations” in Figure 2. However, it is hard to tell which result is more separable from my point of view. A possible suggestion is that the authors could use quantitative clustering metrics to demonstrate, such as Adjusted Rand index. Also, the resolution of figure 2 could be improved.\n- In Experiment 5.2,  the detail of meta-learning network, hypernetwork, is not mentioned in the paper. Is it just a matrix transform? Or a more complicated neural network? The detailed implementation of hypernetwork could be discussed.\n- In Experiment 5.3, The result is not surprising, since the model have used contrastive loss to maximize similarity of “PCP to Same Training Patient”. Therefore, it should have smaller distances, naturally. Also, we encourage the paper to discuss why using the Euclidean distance not the cosine similarity as mentioned in Sec. 3.1.\n- In Experiment 5.4, the paper shows two examples of similar patients, where the performance is hard to evaluate. The reviewer will suggest two ways: (i) using demographics, physiology, or treatment features to match two similar patients, and then quantitatively compare with the matching results given by the paper; (ii) setting distance threshold or cosine similarity threshold to decide whether two patients are similar, and then justify the threshold.\n- In Experiment 5.5. This experiment is very problematic. First, the paper claims and show that the PCP representation could provide the same classification performance as training on the whole training set. It is also not surprising, because when obtaining the PCP representations, the model already uses all the training data, then of course PCP would give the same performance; Second, we encourage the paper to compare with using different proportions of the training set. Often the time, the whole training set could be redundant in ECG setting.\n- The authors are encouraged to analyze the computational complexity of the proposed method.\n- In appendix, figure 9 (bottom) has exactly the same channel signals, which is a significant mistake. Figure 10 has a wrong title description.\n- For dataset PhysioNet 2020 ECG, the paper states that “Each recording can be associated with multiple labels”. It is unclear how to use the dataset for evaluation.\n- The motivation of the paper could be improved. Also, some related works on contrastive learning are missing, for example [17-18].\n\nReferences\n\n[1] RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism. NeurIPS 2016\n\n[2] MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare. NeurIPS 2017\n\n[3] MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals. IJCAI 2019\n\n[4] RAIM: Recurrent Attentive and Intensive Model of Multimodal Patient Monitoring Data. KDD 2018\n\n[5] Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review, Journal of the American Medical Informatics Association 2019\n\n[6] Opportunities and Challenges in Deep Learning Methods on Electrocardiogram Data: A Systematic Review, Computers in Biology and Medicine 2020\n\n[7] Patient subtyping via time-aware LSTM networks, KDD 2017\n\n[8] DDL: Deep Dictionary Learning for Predictive Phenotyping, IJCAI 2019\n\n[9] PEARL: Prototype Learning via Rule Learning, ACM BCB 2019\n\n[10] Identifying Sepsis Subphenotypes via Time-Aware Multi-Modal Auto-Encoder, KDD 2020\n\n[11] Why should I trust you?: Explaining the predictions of any classifier. KDD 2016\n\n[12] A Unified Approach to Interpreting Model Prediction. NeurIPS 2017\n\n[13] Anchors: High-Precision ModelAgnostic Explanation AAAI 2018\n\n[14] CLOCS: Contrastive learning of cardiac signals. arXiv preprint arXiv:2005.13249, 2020. \n\n[15] A simple framework for contrastive learning of visual representations. arXiv preprint arXiv:2002.05709, 2020. \n\n[16] Subject-aware contrastive learning for biosignals. arXiv preprint arXiv:2007.04871, 2020.\n\n[17] Momentum contrast for unsupervised visual representation learning. CVPR 2020\n\n[18] Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748.\n", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper proposes the use of contrastive learning to learn patient specific representations from medical data. The authors show how their method can be used to find similar patients within and across datasets. 

The paper has some issues, as indicated by the reviewers:
- similarity to past work; in the response to R1, the authors specify differences to related papers; however, experimental comparisons should still be performed against CLOCS and DROPS
- the evaluation is not fully convincing (the follow up comments of Reviewer 3), including the retrieval of similar patients
Decision: Reject