Curr Treat Options Cardio Med
`DOI 10.1007/s11936-020-00814-0
`
` (2020) 22:15
`
`State-of-the-Arts Informatics (C Stultz, Section Editor)
`
`Deep Learning
`for Cardiovascular Risk
`Stratification
`Daphne E. Schlesinger1,2,3,4
`Collin M. Stultz, MD, PhD1,2,3,4,5,6,*
`
`Address
`1Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, 02139, USA
`2Institute for Medical Engineering and Science, MIT, Cambridge, MA, 02139, USA
`3Research Laboratory of Electronics, MIT, Cambridge, MA, 02139, USA
`4Computer Science & Artificial Intelligence Laboratory, MIT, Cambridge, MA,
`02139, USA
`5Department of Electrical Engineering and Computer Science, MIT, Cambridge,
`MA, 02139, USA
`*,6Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA
`Email: cmstultz@mit.edu
`
`* The Author(s) 2020
`
`This article is part of the Topical Collection on State-of-the-Arts Informatics
`
`Keywords Risk stratification I Deep learning I Risk models
`
`Abstract
`
`Purpose of review Although deep learning represents an exciting platform for the devel-
`opment of risk stratification models, it is challenging to evaluate these models beyond
`simple statistical measures of success, which do not always provide insight into a model’s
`clinical utility. Here we propose a framework for evaluating deep learning models and
`discuss a number of interesting applications in light of these rubrics.
`Recent findings Data scientists and clinicians alike have applied a variety of deep learning
`techniques to both medical images and structured electronic medical record data. In many
`cases, these methods have resulted in risk stratification models that have improved
`discriminatory ability relative to more straightforward methods. Nevertheless, in many
`instances, it remains unclear how useful the resulting models are to practicing clinicians.
`Summary To be useful, deep learning models for cardiovascular risk stratification must not
`only be accurate but they must also provide insight into when they are likely to yield
`inaccurate results and be explainable in the sense that health care providers can under-
`stand why the model arrives at a particular result. These additional criteria help to ensure
`that the model can be faithfully applied to the demographic for which it is most accurate.
`
`APPLE 1075
`Apple v. AliveCor
`IPR2021-00970
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`Introduction
`
`Accurate risk stratification remains a central theme in all
`stages of the care of patients with cardiovascular disease.
`Indeed, the likelihood that any patient will benefit from
`a given therapeutic intervention is a function, in part, of
`the risk associated with the intervention itself versus the
`risk that the patient will have an adverse event if no
`intervention is performed. Informed clinical decision
`making necessitates gauging patient risk using available
`clinical information.
`A number of societal guidelines recommend the use
`of validated risk scores in the initial evaluation of pa-
`tients with suspected coronary disease [1–3]. The use of
`accurate risk scores helps to ensure that patients who are
`at high risk of adverse outcomes are quickly identified
`and assigned a therapy that is appropriate for their level
`of risk. Nevertheless, risk stratification is far from a
`perfect science, and risk scores often fail to identify
`patients at high risk of inimical outcomes. This problem
`is made more apparent in light of the fact that a relative
`minority of patients with cardiovascular disease experi-
`ence the gravest adverse outcomes. Moreover, while the
`prevalence of adverse events in high-risk populations is,
`by definition, large, the absolute number of events is
`also large in patients who are predicted to be low risk
`using traditional risk prediction metrics. This low risk-
`
`high number dilemma is frequently encountered in many
`areas of cardiovascular clinical research [4]. As such,
`adequately identifying patient subgroups who are truly
`at high risk of adverse events remains a clear unmet
`clinical need. Novel methods are therefore needed to
`realize the full potential of clinical risk stratification
`from existing clinical observations. Machine learning
`and deep learning, in particular, holds the potential to
`robustly identify high-risk patient subgroups, suggest
`personalized interventions that can reduce a given pa-
`tient’s risk, and help ensure that appropriate resources
`are allocated to those patients who are in the most need.
`In this review, we do not strive to review all of the
`relevant literature in the area of deep learning in cardio-
`vascular medicine. Indeed, this review is written for the
`practicing clinician and strives to provide intuitive ex-
`planations for how deep learning models actually work
`and where they are most applicable. As the use of these
`models becomes ubiquitous in the clinical arena, it will
`be important for health care providers to critically eval-
`uate them in order to determine the clinical usefulness
`of any given machine learning approach. Our goal is to
`provide a general framework for understanding what
`advantages these models hold and what considerations
`limit their broad applicability.
`
`Conventional approaches to risk stratification
`
`The term machine learning is believed to have been originated by Arthur Samuel,
`an engineer and scientist who pioneered artificial intelligence in 1959 [5]. He
`described it as “programing computers to learn from experience.” There are diverse
`examples of machine learning in the clinical literature, including straightforward
`approaches like logistic regression and Cox proportional hazards modeling and
`more esoteric techniques like deep learning, which is described in the next section.
`Indeed, the former methods have actually been a part of the clinical literature for
`some time [6–8]. Therefore, while the term machine learning has only recently
`entered the medical lexicon, a number of existing clinical risk scores were devel-
`oped and refined using approaches that fall under this umbrella term. The
`exorbitant list of such models is too lengthy to exhaustively review here. Instead,
`we focus on some approaches that are commonly used to assess patient risk.
`One of the earliest models for quantifying the risk of adverse cardiovascular
`outcomes was developed by Killip et al. in 1967, where 250 patients were
`divided into four simple classes of increasing severity of illness, ranging from
`no clinical signs of heart failure to cardiogenic shock [9]. The primary goal of
`this study was to trial an improved workflow for cardiac intensive care, but the
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`data collected over the course of study revealed patterns in patient survival
`based on their class (now called the Killip class). The utility of these classes for
`identifying high risk patients has been born out in a number of studies, and
`these classes remain a part of the clinical assessment of patients who present
`with an acute myocardial infarction.
`Over time, more sophisticated statistical techniques have been used to
`develop more sophisticated risk stratification models. Both the Framingham
`risk score—which quantifies the risk of adverse events (death from coronary
`heart disease, nonfatal MI, angina, stoke, transient ischemic attack, intermittent
`claudication, and heart failure) in patients who had no prior history of cardiac
`disease—and the Global Registry of Acute Coronary Events (GRACE)
`score—which quantifies all-cause mortality in patients who present with an
`ACS—were developed using Cox proportional hazards regression [10, 11].
`Another class of risk scores, developed from and named for the Thrombolysis
`in Myocardial Infarction (TIMI) study groups, was developed specifically for
`patients who present with symptoms consistent with an acute coronary syn-
`drome. Here, features that were discriminatory with respect to the combined
`outcome of all-cause mortality, new or recurrent MI, or severe recurrent ischemia
`in their cohort were selected using logistic regression. Seven features were selected
`in the final model. To use the risk score itself, the physician simply counts the
`number of features that are present to estimate the short-term risk of either
`mortality after a myocardial infarction post ST segment elevation MI or a com-
`bined outcome of all-cause mortality, new or recurrent MI, or severe recurrent
`ischemia requiring revascularization post non-ST segment elevation ACS [12, 13].
`Regression modeling has found a role for quantifying patient risk in other
`disorders apart from ischemic heart disease. Pocock et al., for example, performed
`a meta-analysis of heart failure patients from 30 different studies, amounting to
`39,372 patients. They used multivariable piecewise Poisson regression methods
`to identify features that are predictive of mortality at 3 years. These features were
`then converted into an integer risk calculator, called the Meta-analysis Global
`Group in Chronic Heart Failure (MAGGIC) score, with higher values correspond-
`ing to greater risk [14]. Similarly, the Seattle Heart Failure Model was developed
`on a cohort of 1125 patients, using a multivariate Cox proportional hazards
`model. This model provides estimates for 1-, 2-, and 3-year mortalities [15, 16].
`Logistic regression and proportional hazard models are advantageous because
`they are easy to interpret: each clinical feature in the model has an associated weight
`that corresponds to how important that feature is for the model arriving at a
`particular result. However, such models are relatively simple and cannot necessarily
`capture complex mechanisms relating observations and outcomes of interest.
`
`What is deep learning?
`
`The diverse, nonuniform terminology in the medical literature unfortunately
`tends to obfuscate the meaning of the term “deep learning.” Deep learning is a
`subfield of machine learning that strives to find powerful abstract representa-
`tions of data using complex artificial neural networks (ANNs) that are then used
`to accomplish some prespecified task. While these abstract data representations
`are powerful ways to describe clinical data, they are difficult to comprehend and
`explain; that is why they are, indeed, “abstract.”
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`ANNs correspond to a class of machine learning algorithms whose algorith-
`mic structure is inspired by structure of the human brain and how it is believed
`that humans compute [17, 18]. A neural network consists of interconnected
`artificial neurons that pass information between one another. A typical ANN
`contains an input layer, which contains several artificial neurons that take
`clinically meaningful data as input. The input layer then passes the clinical data
`to other inner, or “hidden,” layers, each of which performs a series of relatively
`simple computations. At each layer, more abstract representations of the input
`data are obtained. Eventually, the information is passed to an output layer that
`yields a clinically meaningful quantity (Fig. 1).
`Deep learning models, in practice, correspond to neural networks that
`contain several hidden layers. These models, originally referred to as multilayer
`perceptrons, were popularized in the early 1980s for applications such as image
`and speech recognition, then receded in popularity in favor of simpler, easier to
`
`Fig. 1. In our applications, a neural network acts as a function that takes some observations as input and produces some prediction
`of outcomes as the output (a). This function is generated by adding many simple functions (represented by circular nodes that
`process information), each of which takes all the outputs of the previous layer as its input, which renders a network “fully
`connected” (b). These simple functions are strictly increasing and include parameters ( w! ið Þ
`b ið Þ for each node), which are
`chosen by training the network (c). Each layer can be though of an abstraction of the data, which is eventually
`separable in the last layer if the model works well. The output of the last layer is the probability of an adverse
`event, which a clinician may use to inform her clinical decisions (d).
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`train, and perhaps more explainable models [19, 20]. In recent years, however,
`deep neural network (DNN) learning has resurged dramatically both because of
`the availability of so-called “big data” and the development of computational
`methods that facilitate the training of large neural networks. In many of today’s
`applications, these networks can be quite large, having on the order of 105–106
`artificial neurons and millions of modifiable parameters. Parenthetically, as the
`size of clinical datasets is typically much smaller, care must be taken when
`implementing these models to ensure that they are not overtrained.
`While the structure of ANNs, and DNNs in particular, are inspired by the
`structure of neurons in the human brain, these models are best thought of as
`universal function approximators. Indeed, it has been mathematically proven
`that any continuous function on compact spaces can be represented by a neural
`network, under certain constraints [21, 22]. These models therefore form an
`efficient platform for generating functions that model complex relationships
`between patient characteristics/features and outcomes. This highlights an im-
`portant difference between DNNs and simpler methods like logistic regression,
`which models the relationship between outcomes (i.e., the logarithm of the
`odds ratio) and patient features as a linear function. By contrast, a DNN
`corresponds to a complex, highly nonlinear function that takes patient infor-
`mation as input (including medical images) and outputs the corresponding
`outcome. An additional advantage of DNNs is that they can use input data in
`“raw” form, with little preprocessing.
`Deep learning models can, in principle, capture complex, nonlinear, rela-
`tionships between patient features and outcomes and therefore necessarily meet
`the first criteria. However, because these models generate abstract representa-
`tions of the input data, it can be very difficult to understand what the model has
`learned and consequently why the model arrives at a particular result. More-
`over, understanding when the model will fail—i.e., which patients are most
`likely to be associated with an incorrect prediction—can be just as challenging.
`
`Evaluating deep learning risk models
`
`Standard performance metrics, such as the area under the receiver operating
`characteristic curve (AUC), accuracy, and the sensitivity/specificity, provide
`useful information for gauging how a risk model will perform, on average.
`Nevertheless, these metrics do not by themselves offer any interpretative in-
`sights, nor do they help the user understand how the model will perform on any
`individual patient. The upshot being that conventional statistical metrics of
`success are not always sufficient to determine the clinical utility of a deep
`learning model.
`When evaluating applications of machine learning to medical problems,
`there are particular criteria that must be considered given our current under-
`standing of human physiology and the reality of medical practice (Fig. 2). In
`addition to having a level of performance that ensures that it will perform well,
`on average, on the population of interest, ideally a good algorithmic solution
`should also:
`1. Provide information about potential failure modes; i.e., indicate when it is
`likely to yield a false result;
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`Fig. 2. Issues that hinder the clinical acceptance of deep learning models.
`
`2. Be explainable in the sense that clinicians can understand why the model
`arrives at a particular result.
`
`Although determining when a model will fail is challenging, it is an essential
`task. Formally, this can be understood as finding, a priori, patient characteristics
`or subgroups that are associated with incorrect predictions. The development of
`methods that identify such “failure modes” are also a nascent area of research
`within the machine learning community, with most of the published research
`appearing in specialized machine learning conferences or non-peer-reviewed
`online printed archives, with little associated work appearing in the clinical
`literature. Nevertheless, insights into when a model will fail can often be
`garnered if the model itself is explainable; i.e., understanding how/why a model
`arrives a particular result often provides clues as to how the model can yield an
`incorrect result.
`Recently, a new method was described for identifying when a given clinical
`risk score will yield unreliable results [23(cid:129)(cid:129)]. The approach identifies, a priori,
`patient cohorts associated with reduced model accuracy, discriminatory ability,
`and poor calibration. Application to the GRACE risk model correctly identifies
`patient cohorts where the GRACE score has reduced performance. Advantages
`of the method are that it is straightforward to implement and that it can be
`applied to any risk model, regardless of how the risk model was
`developed—thereby making the approach appropriate for deep learning
`models. General methods along these lines will likely play an increasingly
`important role in determining when complex risk models are expected to yield
`useful predictions.
`In addition to deciphering when a given model is likely to fail, developing
`methods that “explain” what a model has learned is an important part of any
`comprehensive strategy that strives to maximize clinical acceptance. Neverthe-
`less, conceptions of explainability or interpretability of machine learning
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`models are diverse, and it is difficult to determine exactly what this term means
`in the context of machine learning models. In his article, “The Mythos of Model
`Interpretability,” Zachary Lipton identifies five types of interpretability for
`machine learning models: trust, causality, transferability, informativeness, and
`fairness [24]. Of particular interest for medical algorithms are causality and
`informativeness. Causality describes if the relationships discovered by the
`model are truly causal or merely correlative. While casualty in machine learning
`is an active area of research, it is always very difficult to tease out causal
`relationships from a retrospective analysis of any dataset [25]. An informative
`deep learning model provides some intuition to support how it arrives at a
`given result. In order to impart useful intuitions, however, one needs to trans-
`late the abstract representations learned by a deep learning model into language
`that is easily understood by the health care practitioner. In short, in the medical
`context, we ideally need models that yield insights that are translatable into the
`language of physiology (Fig. 2).
`There are a limited number of tools that have been used to provide
`interpretations/explanations of what a deep neural network has learned. Shape-
`ly values, Gradient-weighted Class Activation Mapping (Grad-CAM) methods,
`and saliency maps represent a class of methods that can provide insight into
`what input features are most responsible for the risk model making a prediction
`[26–28]. Grad-CAM and saliency maps, in particular, are typically used with
`convolutional neural networks (described below) and provide insight into the
`relative importance of different parts of an image for a specific prediction [29].
`For example, consider a model trained to distinguish between different objects,
`such as dogs and humans. A saliency map may reveal that pixels corresponding
`to the legs (four for a dog and two for a human) are most dispositive. Hence, for
`such a simple task as differentiating humans from dogs, saliency maps provide
`easily understood “explanations.” However, for more complex classification
`tasks, saliency maps may not yield such readily interpretable insights. Indeed,
`these methods generally do not provide information about how the data in
`these regions were used to arrive at a particular decision, nor do they necessarily
`provide any causal insights. More generally, it has been argued that the attempts
`to explain deep models are inherently flawed because such post hoc explana-
`tions can never have true fidelity with respect to the original complex model
`[30(cid:129)]. In this vein, the use of interpretable models have an advantage in that
`they are designed to yield explanations that can be understood by domain
`experts. Nevertheless, it is not clear that commonly used interpretable models
`can capture the complex nonlinear relationships described above in manner
`that yields clear explanations. A compromise may be to build models that
`combine both mechanistic/physiologic models and deep learning models to
`enhance both model explainability and predictive performance. This is an active
`area of research.
`It has been argued that clinicians should embrace black box models rather
`than strive to develop explanations that provide insight into how the model
`arrives at a particular result [31]. Proponents of this thesis argue that clinical
`decision-making is frequently rooted in an incomplete understanding of the
`disease process in question and how the potential intervention actually works.
`Hence requiring deep learning models to be explainable holds them to a higher
`standard than other methods used to inform clinical decision making and
`further stymie innovation in this space.
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`While there is merit to this argument, there is little doubt that clinical
`decisions are grounded in some understanding of the disease process. Indeed,
`it is precisely this, albeit imperfect, understanding that guides our therapeutic
`choices. By contrast, deep learning models represent an unprecedented level of
`opaqueness with respect to clinical understanding. In the setting of black
`models and only statistical measures of the model’s overall performance,
`additional information are needed to determine when a model prediction is
`appropriate for a specific patient. While the identification of model failure
`modes and explainability are distinct concepts, they are related. Failure mode
`analyses strive to identify patient subgroups where the model has reduced
`performance, and a comprehensive understanding of how a complex model
`arrives at a particular result provides further assurance that the model is appro-
`priate for a given patient, who has a given set of clinical characteristics. Expla-
`nations that are inconsistent, for example, with our understanding of the
`underlying pathophysiology should not be trusted.
`In sum, it is our view that deep learning models for any clinical application
`should be evaluated using these metrics, in addition to standard statistical
`measures of performance. In what follows we discuss several recent applications
`of deep learning methods for cardiovascular risk stratification and evaluate
`them relative to the metrics discussed above.
`
`Deep learning for risk prediction
`
`Deep learning for image classification has a relatively extensive literature.
`Indeed, the Imagenet challenge—a worldwide competition for classifying mil-
`lions of curated images—has led to the development of many sophisticated
`algorithms for image classification [32]. In a number of applications, these
`image classification algorithms have been modified and fruitfully applied to
`clinical images to quantify patient risk. However, these methods have mainly
`been used for automatic disease diagnosis from pathology slides and radiolog-
`ical scans [33–38]. These algorithms are usually implemented using a class of
`DNNs called convolutional neural networks (CNNs). CNNs are inspired by the
`structure of the mammalian visual cortex, where each neuron “sees” a small
`region of the visual field, called the receptive field of that neuron [39]. In a
`CNN, the information contained in adjacent groups of pixels of an image,
`analogous to the receptive field, is summarized, using a mathematical opera-
`tion called a convolution to create an abstraction of the information in the
`image [40].
`In cardiology, deep learning work has been focused on the automatic
`interpretation of cardiac images, with few applications to the development of
`models that directly quantify patient risk [41]. Recent studies have highlighted
`the ability of CNNs to identify echocardiographic windows using the images
`alone [42, 43], correctly segment the left ventricle in both cardiac CT images and
`cardiac MRIs [44, 45], and accurately detect cardiac MR motion artifacts [46].
`The use of CNNs to garner insights into the risk of future adverse outcomes,
`however, is still a nascent area of investigation.
`A recent study that purports to use medical image data for assessing cardio-
`vascular risk was published by Poplin et al. [47(cid:129)(cid:129)]. In that work, the authors
`used a CNN to predict age, gender, smoking status, systolic blood pressure,
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`diastolic blood pressure, and, most importantly, major adverse cardiovascular
`events (MACE) within 5 years from the time that retinal fundus images were
`acquired. The dataset used to develop and validate the model was obtained
`from the UK Biobank and EyePACS (a retinal image database consisting of
`images obtained during routine diabetic screening in clinics in the USA). They
`report an AUC of 0.70 for predicting MACE after 5 years using their deep
`algorithm. This performance exceeds that of predictions made based on single
`risk factors such as age and systolic blood pressure. However, they do not
`outperform an existing, simpler proportional hazards model, SCORE (System-
`atic COronary Risk Evaluation), proposed by Conroy et al. in 2003 [48]. In
`addition to predicting risk, they utilized saliency maps, described above, to
`attempt to explain their algorithm. Saliency maps highlight portions of the
`retinal images that contributed significantly to the predictions their models
`produced. However, the usefulness of these saliency maps is limited because
`they give us no information about the mechanism by which certain features of
`the retina relate to cardiovascular risk and if the deep learning model has
`recapitulated that mechanism.
`Recently, there have been attempts to extend classification algorithms,
`which were originally designed to analyze medical images, to different
`types of data in the Electronic Medical Record (EMR). The EMR can be
`divided into two types of data: structured data and unstructured data.
`Structure medical data refers to what can be found in the pre-existing
`fields with the electronic medical record; e.g., lab results, vital signs, and
`demographic information. Unstructured data refers to what appears in
`medical notes written by health care practitioners. In a recent study,
`Mayampurath et al. assembled structured data from the electronic health
`record into a visual format that could then be used to train a CNN to
`predict in-hospital outcomes [49]. Essentially, the EMR is converted to a
`two-dimensional medical
`image, which enables the use of standard
`machine learning techniques appropriate for medical image processing.
`The image itself maps time on one axis and 156 clinical variables
`(including vital signs,
`laboratory results, medications, diagnostic tests,
`and nurse examinations), recorded over the first 48 h of admission, on
`the other axis. Overall, the discriminatory ability of the best performing
`CNN (the authors considered more than one) was 0.91, suggesting that
`the method holds considerable promise.
`A significant advantage here is that they can leverage methods used
`to “interpret” what CNNs have learned about images to help explain
`why their deep learning model arrives at a particular result. In their
`work, the authors used a standard method—Gradient-weighted Class
`Activation Mapping or Grad-CAM—to understand what clinical features
`are most important for discriminating between patients who die in-
`hospital and those who do not [28]. Not surprisingly, the method finds
`that vital signs, interventions (e.g., mechanical ventilation), and admin-
`istered medications were important
`for distinguishing between those
`who would have an in-hospital event and those who would not. Of
`interest, the model does suggest that simple nursing examinations, rep-
`resented by Morse and Braden scores, may be important for predicting
`in-hospital mortality. Moreover,
`it is noteworthy that there are many
`different ways to organize data arising from the EMR into two-
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`dimensional representations and not all visual representations will have
`the same prognostic information. The authors of this study only exper-
`iment with three different ways to organize the data.
`While these results are encouraging, the problem of predicting in-
`hospital mortality using 48 h of admission data may be, relatively
`speaking, not that difficult. For example, one would likely do fairly well
`predicting in-hospital mortality using a simplified set of input features
`that includes where the patient is admitted (ICU vs. hospital floor), vital
`signs trajectories during the first 48 h (higher death rates are expected in
`patients who become hypotensive soon after admission), and whether
`the patient requires mechanical ventilation or inotropic support soon
`after admission. As the authors do not compare their method to what
`would be obtained using a simple method such as logistic regression
`model using a rich set of clinical features, it is not clear whether a CNN
`is truly necessary for this task.
`One very popular data source for machine learning is the electrocar-
`diogram because it
`is routinely measured, cheap to administer, and
`apparently rich in information, some of which may not be easily dis-
`cernable by humans. In addition, a variety of deep learning methods
`exist
`that can effectively deal with time series data, much like that
`arising from a single lead and multiple lead ECGs. Many of
`these
`approaches have already been applied to the interpretation and classifi-
`cation of electroencephalographic signals [50].
`Attia et al. also mined the ECG for new information by attempting to
`predict left ventricular systolic dysfunction from the 12-lead ECG and
`transthoracic echocardiogram (TTE) using a convolutional neural net-
`work [51]. As LV dysfunction itself is a powerful predictor of subsequent
`heart failure, the resulting network indirectly identifies patients at ele-
`vated risk of adverse events [52]. By traditional statistical metrics (e.g.
`AUC) their classifier performed extremely well, with some exceptions
`(positive predictive value). The low positive predictive value (PPV) tells
`us that the model has many false positives, but, crucially, this does not
`help us predict when the model will fail; i.e., for which type of patients.
`The work also does not provide insights on the details of the relation-
`ship between the ECG and ALVD. For example, some determination
`about what segments of the ECG contribute to the prediction would
`be highly informative and of scientific interest.
`Myers et al. applied a recurrent neural network (RNN)—a structure
`used to analyze time-series data—to continuous ECG data, along with a
`set of patient features, to predict the risk of death 1 year after non-ST
`segment elevation myocardial infarction (NSTEMI) [53]. For these stud-
`ies, samples from the ST segments of each beat were identified and
`extracted in an automated fashion and then used as input
`to the
`RNN. The resulting neural network, which incorporates information
`from approximately 1 min of continuous ECG data, had improved
`predictive and discriminatory ability relative to a logistic regression
`model that used the same patient features and summary information
`from the admission 12-lead ECG. Nevertheless, the complexity of the
`model makes it difficult
`to understand precisely how and why the
`model arrives at a particular result. Consequently, while the model itself
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`has improved performance relative to existing methods, the ultimate
`clinical utility of the method remains to be determined.
`
`“All models are wrong, but some are useful”
`
`The recent, notable successes of deep learning approaches argue that they will
`have place in the pantheon of methods used to build risk stratification models.
`However, it is not always clear when these approaches should be chosen