NONINVASIVE METHODS FOR DETECTION OF PULMONARY HYPERTENSION

Provided herein are methods, systems, and computer program products for the detection of pulmonary hypertension comprising receiving voltage-time data of a plurality of leads of an electrocardiograph of a subject; generating a feature vector from the voltage-time data; providing the feature vector to a pretrained learning system; and receiving from the pretrained learning system an indication of the presence or absence of pulmonary hypertension in the subject.

BACKGROUND

Embodiments of the present disclosure relate to methods for the diagnosis and treatment of pulmonary hypertension. Pulmonary hypertension (PH) is a life-threatening disease estimated to affect 1% of the global population and up to 10% of patients over 65 years of age. The timely diagnosis of PH is imperative not only for effective therapeutic intervention but also to amplify the odds of survival. Multiple studies suggest that earlier diagnosis, by even a few months, can lead to dramatic increases in quality of life and lifespan extension. However, the symptoms of PH are non-specific and very similar to the symptoms seen in other common diseases, including asthma, chronic obstructive pulmonary disease (COPD), and heart failure. This makes suspicion of PH low within ambulatory care settings, thus timely referral to pulmonologists or cardiologists who can confirm diagnosis is critical. Currently, delay in diagnosis can occur with an average time of 2.5 years (from onset of symptoms to diagnosis) and up to 4 years in some cases, primarily due to delayed referral to the appropriate specialists. Indeed, because the gold standard for the definitive diagnosis of PH is right heart catheterization (RHC), an invasive procedure that entails non-negligible risks, physicians often hesitate to proceed until all other diseases have been sequentially ruled out. Thus, there is a great need for new and improved methods, e.g., diagnostic methods, for early and accurate detection of PH. Accordingly, embodiments of the invention disclosed herein include algorithms applied to electrocardiograms (ECGs), a non-invasive procedure, in the diagnostic workup of PH for the detection of PH and stratification of patients based on the risk of PH, allowing earlier diagnosis and intervention.

BRIEF SUMMARY

According to embodiments of the present disclosure, methods of and computer program products for the detection of pulmonary hypertension are provided.

In some aspects of the invention, disclosed herein are methods comprising receiving voltage-time data of a subject, the voltage-time data comprising voltage data of a plurality of leads of an electrocardiograph; generating a feature vector from the voltage-time data; providing the feature vector to a pretrained learning system; and receiving from the pretrained learning system an indication of the presence or absence of pulmonary hypertension in the subject.

Aspects of the invention, as disclosed herein, also include a system comprising: an electrocardiograph comprising a plurality of leads; a computing node comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor of the computing node to cause the processor to perform a method comprising: receiving voltage-time data of a subject from the echocardiograph, the voltage-time data comprising voltage data of the plurality of leads; generating a feature vector from the voltage-time data; providing the feature vector to a pretrained learning system; and receiving from the pretrained learning system an indication of the presence or absence of pulmonary hypertension in the subject.

In certain aspects of the invention, disclosed herein is a computer program product for detection of pulmonary hypertension, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform a method comprising: receiving voltage-time data of a subject from the echocardiograph, the voltage-time data comprising voltage data of the plurality of leads; generating a feature vector from the voltage-time data; providing the feature vector to a pretrained learning system; and receiving from the pretrained learning system an indication of the presence or absence of pulmonary hypertension in the subject.

DETAILED DESCRIPTION

Convolutional neural networks offer a comprehensive approach to analyzing and interpreting the vast amount of data generated in a single ECG. Algorithms were developed using retrospective Mayo Clinic, patient-level data; including ECGs, procedural measurements, physician notes, and patient demographics for the purpose of screening patients for PH. In order to distinguish between PH and non-PH patients for model training, each ECG was paired with either an RHC or an echocardiogram. Measurements derived from these procedures were used to define the cohorts. This yielded 65,994 unique patients (11,238 PH and 54,756 non-PH) of which 48% were used in model training, 12% held-out for preliminary validation, and a final 40% held-out for testing.

All models used voltage-time information from 12-lead ECGs as inputs. Modeling techniques explored included convolutional neural networks with differing structures such as using all 12 leads as a single input, groups of 3 leads as separate inputs, each lead converted to spectrogram, and combinations of these methods. Additionally, two distinct preliminary models were created, one in which the ECG was performed within a month of the patient's diagnosis (diagnostic model) and another in which the ECG was performed 6 to 18 months prior to the diagnosis date (pre-emptive model). Based on relative performance at that time, the preliminary diagnostic model was selected for further development.

The best performing preliminary diagnostic model was a convolutional neural network with residual connections incorporating the 12-lead single input. The updated diagnostic model obtained an area under the curve (AUC) of 0.94 on the diagnostic validation and test sets, and was able to distinguish PH 6 to 18 months prior to diagnosis with an AUC of 0.90 on the validation and test sets. Finally, ECGs taken 3-5 years prior to diagnosis did not exhibit a significant decrease in performance, with AUCs above 0.8466. Ultimately, these results show a strong signal within ECGs for detecting PH and could be implemented in ECG machines in primary and secondary care settings to accelerate patient diagnosis and intervention. Additionally, the disease also comprises an underlying genetic component, which is supported by the detection of these diagnostic signals 3-5 years prior to diagnosis. The methods disclosed herein may be coupled with a genetic panel to provide further specificity and sensitivity. Similarly, such methods may be used with genetic panels to detect novel biomarkers of disease.

Accordingly, in some aspects of the invention, disclosed herein are methods comprising receiving voltage-time data of a subject, the voltage-time data comprising voltage data of a plurality of leads of an electrocardiograph; generating a feature vector from the voltage-time data; providing the feature vector to a pretrained learning system; and receiving from the pretrained learning system an indication of the presence or absence of pulmonary hypertension in the subject. Generating the feature vector may comprise generating a spectrogram based on the voltage data of the plurality of leads. In some embodiments generating the feature vector comprises grouping the voltage data of the plurality of leads into a plurality of subsets.

In some embodiments, such methods further comprise receiving demographic information of the subject, wherein generating the feature vector comprises adding the demographic information to the feature vector. In some such embodiments, the learning system comprises a convolutional neural network. Such convolutional neural networks may comprise at least one residual connection.

In some embodiments the voltage-time data of a subject is received from an electrocardiograph. In further embodiments, the voltage-time data of a subject is received from an electronic medical record.

In some embodiments, the method further comprises providing the indication to an electronic health record system for storage in a health record associated with the subject. In some embodiments, the method further comprises providing the indication to a computing node for display to a user.

With reference now toFIG. 1, a system for detecting pulmonary hypertension is illustrated according to embodiments of the present disclosure. As outlined above, in various embodiments, patient information, including electrocardiogram (ECG) data, is provided to a learning system in order to determine the presence of pulmonary hypertension. Thus, aspects of the invention, as disclosed herein, also include a system comprising: an electrocardiograph comprising a plurality of leads; a computing node comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor of the computing node to cause the processor to perform a method comprising: receiving voltage-time data of a subject from the echocardiograph, the voltage-time data comprising voltage data of the plurality of leads; generating a feature vector from the voltage-time data; providing the feature vector to a pretrained learning system; and receiving from the pretrained learning system an indication of the presence or absence of pulmonary hypertension in the subject. Generating the feature vector may comprise generating a spectrogram based on the voltage data of the plurality of leads. In some embodiments generating the feature vector comprises grouping the voltage data of the plurality of leads into a plurality of subsets.

In some embodiments, such systems further comprise receiving demographic information of the subject, wherein generating the feature vector comprises adding the demographic information to the feature vector. In some such embodiments, the learning system comprises a convolutional neural network. Such convolutional neural networks may comprise at least one residual connection.

In some embodiments the voltage-time data of a subject is received from an electrocardiograph. In further embodiments, the voltage-time data of a subject is received from an electronic medical record.

In some embodiments, the system further comprises providing the indication to an electronic health record system for storage in a health record associated with the subject. In some embodiments, the system further comprises providing the indication to a computing node for display to a user.

Patient data may be received from electronic health record (EHR)101. An electronic health record (EHR), or electronic medical record (EMR), may refer to the systematized collection of patient and population electronically-stored health information in a digital format. These records can be shared across different health care settings. Records may be shared through network-connected, enterprise-wide information systems or other information networks and exchanges. EHRs may include a range of data, including demographics, medical history, medication and allergies, immunization status, laboratory test results, radiology images, vital signs, personal statistics like age and weight, and billing information. EHR systems may be designed to store data and capture the state of a patient across time. In this way, the need to track down a patient's previous paper medical records is eliminated.

Electrocardiogram (ECG) data may be received directly from an electrocardiography device102. In an exemplary 12-lead ECG, ten electrodes are placed on the patient's limbs and on the surface of the chest. The overall magnitude of the heart's electrical potential is then measured from twelve different angles (leads) and is recorded over a period of time (usually ten seconds). In this way, the overall magnitude and direction of the heart's electrical depolarization is captured at each moment throughout the cardiac cycle.

Additional datastores103, may include further patient information as set out herein. Suitable datastores include databases, flat files, and other structures known in the art.

It will be appreciated that ECG data may be stored in an EHR for later retrieval. It will also be appreciated that ECG data may be cached, rather than delivered directly to a learning system for further processing.

Learning system104receives patient information from one or more of EHR101, ECG102, and additional datastores103. As set out above, in some embodiments, the learning system comprises a convolutional neural network. In various embodiments, the input to the convolutional neural network comprises voltage-time information an ECG, which in some embodiments is paired with additional patient information such as demographics or genetic information.

Learning system104may be pretrained using suitable population data as set out in the examples in order to produce an indication of the presence or absence of pulmonary hypertension. In some embodiments, the indication is binary. In some embodiments, the indication is a probability value, indicating the likelihood of pulmonary hypertension given the input patient data.

In some embodiments, learning system104provides the indication of pulmonary hypertension for storage as part of an EHR. In this way, a computer-aided diagnosis is provided, which may be referred to by a clinician. In some embodiments, learning system104provides the indication of pulmonary hypertension to a remote client105. For example, a remote client may be a health app, a cloud service, or another consumer of diagnostic data. In some embodiments, the learning system104is integrated into an ECG machine for immediate feedback to a user during testing.

In some embodiments, a feature vector is provided to a learning system. Based on the input features, the learning system generates one or more outputs. In some embodiments, the output of the learning system is a feature vector.

In some embodiments, the learning system comprises a SVM. In other embodiments, the learning system comprises an artificial neural network. In some embodiments, the learning system is pre-trained using training data. In some embodiments training data is retrospective data. In some embodiments, the retrospective data is stored in a data store. In some embodiments, the learning system may be additionally trained through manual curation of previously generated outputs.

In some embodiments, the learning system, is a trained classifier. In some embodiments, the trained classifier is a random decision forest. However, it will be appreciated that a variety of other classifiers are suitable for use according to the present disclosure, including linear classifiers, support vector machines (SVM), or neural networks such as recurrent neural networks (RNN).

Suitable artificial neural networks include but are not limited to a feedforward neural network, a radial basis function network, a self-organizing map, learning vector quantization, a recurrent neural network, a Hopfield network, a Boltzmann machine, an echo state network, long short term memory, a bi-directional recurrent neural network, a hierarchical recurrent neural network, a stochastic neural network, a modular neural network, an associative neural network, a deep neural network, a deep belief network, a convolutional neural networks, a convolutional deep belief network, a large memory storage and retrieval neural network, a deep Boltzmann machine, a deep stacking network, a tensor deep stacking network, a spike and slab restricted Boltzmann machine, a compound hierarchical-deep model, a deep coding network, a multilayer kernel machine, or a deep Q-network.

In machine learning, a convolutional neural network (CNN) is a class of feed-forward artificial neural networks applicable to analyzing visual imagery and other natural signals. A CNN consists of an input and an output layer, as well as multiple hidden layers. The hidden layers of a CNN typically consist of convolutional layers, pooling layers, fully connected layers and normalization layers. Convolutional layers apply a convolution operation to the input, passing the result to the next layer. The convolution emulates the response of an individual neuron to stimuli. Each convolutional neuron processes data only for its receptive field.

A convolution operation, allows a reduction in free parameters as compared to a fully connected feed forward network. In particular, tiling a given kernel allows a fixed number of parameters to be learned irrespective of image size. This likewise reduces the memory footprint for a given network.

A convolutional layer's parameters consist of a set of learnable filters (or kernels), which have a small receptive field, but extend through the full depth of the input volume. During the forward pass, each filter is convolved across the width and height of the input volume, computing the dot product between the entries of the filter and the input and producing a 2-dimensional activation map of that filter. As a result, the network learns filters that activate when it detects some specific type of feature at some spatial position in the input.

In an exemplary convolution, a kernel comprises a plurality of weights w1. . . w9. It will be appreciated that the sizes provided here are merely exemplary, and that any kernel dimension may be used as described herein. The kernel is applied to each tile of an input (e.g., an image). The result of each tile is an element of a feature map. It will be appreciated that a plurality of kernels may be applied to the same input in order to generate multiple feature maps.

Stacking the feature maps for all kernels forms a full output volume of the convolution layer. Every entry in the output volume can thus also be interpreted as an output of a neuron that looks at a small region in the input and shares parameters with neurons in the same feature map.

Convolutional neural networks may be implemented in various hardware, including hardware CNN accelerators and GPUs.

Referring now toFIG. 2, a flowchart is provided illustrating a method of detecting pulmonary hypertension according to embodiments of the present disclosure. At201, voltage-time data of a subject is received. The voltage-time data comprises voltage data of a plurality of leads of an electrocardiograph. At202, a feature vector is generated from the voltage-time data. At203, the feature vector is provided to a pretrained learning system. At204, an indication of the presence or absence of pulmonary hypertension in the subject is received from the pretrained learning system.

Referring now toFIG. 3, a schematic of an example of a computing node is shown. Computing node10is only one example of a suitable computing node and is not intended to suggest any limitation as to the scope of use or functionality of embodiments described herein. Regardless, computing node10is capable of being implemented and/or performing any of the functionality set forth hereinabove.

The present disclosure may be embodied as a system, a method, and/or a computer program product. For example, in some aspects or the invention, provided herein is a computer program product for detection of pulmonary hypertension, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform a method comprising: receiving voltage-time data of a subject from the echocardiograph, the voltage-time data comprising voltage data of the plurality of leads; generating a feature vector from the voltage-time data; providing the feature vector to a pretrained learning system; and receiving from the pretrained learning system an indication of the presence or absence of pulmonary hypertension in the subject. Generating the feature vector may comprise generating a spectrogram based on the voltage data of the plurality of leads. In some embodiments generating the feature vector comprises grouping the voltage data of the plurality of leads into a plurality of subsets.

In some embodiments, such computer program products further comprise receiving demographic information of the subject, wherein generating the feature vector comprises adding the demographic information to the feature vector. In some such embodiments, the learning system comprises a convolutional neural network. Such convolutional neural networks may comprise at least one residual connection.

In some embodiments the voltage-time data of a subject is received from an electrocardiograph. In further embodiments, the voltage-time data of a subject is received from an electronic medical record.

In some embodiments, the computer program product further comprises providing the indication to an electronic health record system for storage in a health record associated with the subject. In some embodiments, the system further comprises providing the indication to a computing node for display to a user.

The computer program product provided herein may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure.

EXAMPLES

A diverse set of cohorts were generated for preliminary model development using a combination of mean pulmonary arterial pressure (mPAP) measured during RHC and tricuspid regurgitation velocity (TRV) measured during echocardiogram (Table 1). RHC is the gold standard for PH diagnosis, with an mPAP greater than or equal to 21 mmHg denoting PH (recently lowered from the previous threshold of 25 mmHg). TRV measurements are less conclusive, i.e., while TRV less than or equal to 2.8 m/s indicates the absence of PH and TRV greater than or equal to 3.4 m/s indicates its presence, there is an intermediate range for which diagnosis is inconclusive using TRV alone and other measured must be considered.

Additionally, one cohort was generated using diagnosis extracted from the clinical notes, coupled with echo measurements to test the capabilities of augmented curation (Cohort 7). This cohort was generated using a subset of patients with echocardiogram measurements, which accounts for the lower number of PH patients. As a first step toward this end, Mayo physicians provided a positive control cohort of 1,630 patients, hereafter referred to as the Mayo PH Cohort. To expand this cohort an additional 19,504 patients that contained the term “pulmonary hypertension” were identified within their notes, hereafter referred to as the Potential PH Cohort. A BERT model was trained to classify the sentiment regarding a PH diagnosis. This model was used to distinguish between true positive and negative patients in the Mayo and Potential PH Cohorts, and how such a model could scale to additional diseases and features.

Example 2: Training a BERT Model for Diagnoses

As a first step toward creating a BERT model for diagnosis, the Nference® Signals application was used to determine the top 250 phenotypes most closely associated to “pulmonary hypertension” and sentences from the Mayo corpus of clinical notes were extracted for these phenotypes. Sentences were manually classified by MD- and PhD-level scientists at nference into categories, e.g., positive (YES), negative (NO), suspected (MAYBE), and alternate context (OTHER), with examples shown in Table 2.

TABLE 2Manual classificationsPositive diagnosis (YES)Negative diagnosis (NO)“In regard to Ms. LNAME's history of atrial fibrillation, she“She has no history of chronic lung disease, rheumatic fever, orreports that she had occasional short episodes of a rapid rate thatendocarditis.”lasts a minute”“Although her right ventricle is compromised she is quite well“He has severe tricuspid regurgitation due to a partial flailcompensated hemodynamically without evidence oftricuspid leaflet and a history of anemia.”right ventricular failure.”Suspected Diagnosis (MAYBE)Alternate Context (OTHER)“However, this could also be due to interstitial lung diseases“I have also sent him a copy of our recent paper on smoking-such as sarcoidosis, hypersensitivity pneumonitis, or Langerhansrelated interstitial lung diseases.”cell histocytosis.”“She has no family history of lung fibrosis or other lung diseases.”“Because of the swelling I wonder about the possibility of corpulmonale and underlying pulmonary hypertension.”

Optionally, additional categories may be added to this training set to support increased model granularity, e.g., separating out family history and/or disease risk resulting from medication (both encompassed by OTHER as exemplified above).

A Deep Model Builder application was developed for sentence tagging, with a user interface that improved efficiency while also tracking the changes made across multiple users. The first model was generated on 11,433 sentences and had on overall accuracy of 0.85, defined as the fraction of labels the model correctly predicted over the total sentences. The Deep Model Builder not only enabled the user to review tagged sentences that the model classified incorrectly but could also be used to run the model on an untagged set of sentences, again improving downstream efficiency of the now “augmented curation”. As shown in Table 3, with multiple cycles of augmented curation, the accuracy of the model improved from 0.85 to 0.936.

TABLE 3Improved model accuracy11433 Total Sentences7409 tagged as YES1328 tagged as NO1469 tagged as MAYBE1195 tagged as OTHERUsing all classification labelsAccuracy: 0.85LabelPrecisionRecallF1-ScoreOther0.670.640.66Maybe0.650.610.63No0.790.890.84Yes0.930.930.9315,121 Total Sentences9615 tagged as YES1724 tagged as NO1930 tagged as MAYBE1852 tagged as OTHERUsing all classification labelsAccuracy: 0.91LabelPrecisionRecallF1-ScoreOther0.810.750.78Maybe0.730.780.76No0.880.930.91Yes0.960.950.9618,490 Total Sentences11,505 tagged as YES2265 tagged as NO2235 tagged as MAYBE2485 tagged as OTHERUsing all classification labelsAccuracy: 0.936LabelPrecisionRecallF1-ScoreOther0.810.910.86Maybe0.870.830.85No0.970.950.96Yes0.970.960.96

Because the aforementioned model was trained on 250 different PH-related phenotypes, the sentences used to train this model were primarily discussing diseases related to cardiology, pulmonology, and metabolic disorders. Given the breadth of the phenotypes already captured by the model, it suggests the model is robust enough to scale to additional therapeutic areas, ranging from COVID-19 to oncology, with minimal retraining (˜1000-3000 sentences). Some areas require additional curation to capture specific language or context in that particular field.

To extract the context around a new feature or more specific association, a new model would need to be trained. This is where the augmented curation becomes critical, as it enables the flexibility to create a specific, refined model for any use case. Other methods that structure textual data must predefine the fields needed for manual curation and thus cannot match the speed with which the disclosed method can train a new model for any feature of interest.

Example 3: Using the Diagnosis Model for Cohort Selection

Before running the BERT model on the Potential PH Cohort to identify additional PH patients, it was run on the Mayo PH Cohort to assess the distribution of sentence sentiment for a positive control. Here, approximately 180,000 sentences for these patients containing the term “pulmonary hypertension” were classified by the model. As shown inFIG. 4, on average 68% of sentences were classified as YES sentiment, only 2% as NO, 7% as MAYBE, and 23% as OTHER, an excellent validation of our model and positive cohort.

The sentiment analysis shown above was also used to identify patients in the Mayo PH Cohort that did not have PH according to their clinical notes. Of the 1,630 patients with clinical notes that were provided, sentiment analysis and subsequent manual review identified 35 patients in this cohort that did not have PH. An example of this semi-automated workflow is shown inFIG. 5. Here, the distribution contains PH negative patients, resulting in a longer tail for the NO classification. For the 25 patients in this particular tail, the nference applications built within the Mayo environment were used to examine each mention of “pulmonary hypertension” in these patients' notes, resulting in 7 patients with PH, 2 with suspected PH, and 16 without PH. The remaining 19 patients within this cohort without PH were identified in a previous iteration. Removing these 35 patients, representing over 2% of the Mayo PH Cohort, could prove significant for model performance.

After validating the diagnosis model on the Mayo PH Cohort, the model was run on sentences containing “pulmonary hypertension” for the 19,504 patients in the Potential PH Cohort. As shown inFIG. 6, the average YES sentiment of 58% is lower than the Mayo PH Cohort, but this result can primarily be accounted for by the 30% of patients without a YES sentence. Similarly, almost 80% of patients do not have a sentence with NO sentiment, meaning that nference could potentially increase the PH positive control set by an order of magnitude.

To automate the differentiation between positive and negative PH patients in these cohorts, various logistic regression models were tested using a combination of augmented curation results and/or echocardiogram measurements, TRV, and estimated right atrial pressure (RAP). Features used to describe a patient via augmented curation included the percent of sentences with Yes, No, Maybe, and Other sentiment, as well as the number of PH occurrences per note. Features used for TRV and RAP included the mean, median, minimum, maximum, and standard deviation of each measurement. A positive control cohort of 1556 patients who had positive diagnoses and echocardiogram measurements was generated from the Mayo PH Cohort. A negative control cohort was generated through manual curation of records for patients with TRV and RAP measurements. Models were evaluated using 10-fold cross validation and a 90:10 train-test split.

As shown in inFIG. 7, coupling augmented curation with echocardiogram measurements performs better than either alone. Yet augmented curation performs much better than echocardiogram measurements alone. This was expected as the goal of augmented curation is to capture the physician's interpretation of the sum total of the patient's records.

Two hundred patients were randomly sampled as a holdout set, and their records were manually curated to determine whether the patient was diagnosed with PH or not. One patient withdrew consent and was subsequently excluded. Of the remaining 199 patients, 191 were classified correctly by the logistic regression model or 95.9%.

Example 4: Algorithm Development and Results

For each cohort, preliminary models were evaluated on two different time windows: 1 month on either side of the diagnosis date (diagnosis window) and 6-18 months prior to diagnosis (pre-emptive window). For the preliminary models, all ECGs were considered for the negative patients. For the updated model, ECGs for negative patients were limited to those prior to the last procedure used to classify said patients. All ECGs taken when the patient was younger than 18 years of age were excluded. For each cohort, patients were split into train (48%), test (40%), and validation (12%) sets.

Two performance metrics were used to evaluate each model: patient-wise area under the curve (AUC) and age-gender-wise AUC. Patient-wise AUC randomly sampled one ECG per patient and the mean of 50 random runs was reported. Patient-wise AUC ensure patients with more ECGs, i.e., potentially sicker patients, are not over-represented. Age-gender-wise AUC randomly sampled 4 negative ECGs for each positive ECG matched by age and gender at the time the ECG was taken. If 4 negative ECGs are not available, positive ECGs are under-sampled to maintain a 1:4 positive-negative ECG ratio. Here again, the mean of 50 random runs is reported. The advantage here is that the age and gender distributions are maintained between the positive and negative cohorts.

Algorithms were developed testing single-branch, four-branch, and twelve-branch 1D convolutional neural networks (CNNs), using 12-lead voltage-time signals as one input, four groups of three leads, and individual leads, respectively. Other varied parameters included age and gender as inputs, an additional 2D spectrogram, residual connections, and window size (i.e., a ten second window vs. overlapping two second windows). (see Table 4 for results from a preliminary model) An optimal model architecture was found using a single-branch 1D CNN with residual connections and overlapping two second windows (FIG. 8—shown for updated model). Age and gender were not required as inputs and inclusion of a 2D spectrogram did not dramatically increase performance (data not shown). Models were also trained using ECGs with sinus rhythm alone or by excluding patients with pacemakers, but neither modification dramatically improved performance (see Table 5 and 6 for results from a preliminary model). Finally, we tested the diagnostic model on ECGs from the pre-emptive window (FIG. 9—shown for updated model) and found that the diagnostic model performed better than the pre-emptive model for these ECGs (data not shown). The preliminary diagnostic model trained on Cohort 3 was one of the best performing models and was used for further study. We used the updated diagnostic model to test ECGs from 0-5 year prior to diagnosis in 6-month windows (FIG. 10—shown for updated model).

Example 5: Identification of Putative Genetic Biomarkers that May Explain Early ECG Signals Related to Pulmonary Hypertension

The finding that the ECG-based model for diagnosing pulmonary hypertension maintains performance in identifying pulmonary hypertension patients for up to five years prior to the diagnosis date implies that the ECG signal provides information about a patient's long-term susceptibility to pulmonary hypertension. Without wishing to be bound by theory, one possible explanation for this is that certain germline genetic mutations that predispose patients to pulmonary hypertension also modulate cardiac electrophysiology, and are therefore detectable in the ECG long before pulmonary hypertension diagnosis. The nferX® platform was employed to identify genetic mutations associated with pulmonary hypertension and subsequently triangulate evidence that supports the notion that these genes modulate ECG signals. Six candidate genes including two potassium channels, KCNK3 and KCNA5, and 4 additional genes including CAV1, SMAD4, GJB2, TBX4, were identified. (See Table 7)

The nferX Human Genetics application reveals that 26 genes have been associated with pulmonary hypertension (FIG. 11). Of these genes, two, KCNK3 and KCNA5, are potassium channels that are expressed in cardiac tissue (FIG. 12). We prioritized KCNK3 and KNCA5 for further investigation because potassium channels are known contributors to cellular action potentials; the cardiac action potential is the basis of the signal observed by the ECG. The nferX Single Cell application demonstrates that, at the single cell level, KCNK3 and KCNA5 are expressed strongly in neuronal and cardiac cell types, respectively (FIG. 13). At lower expression levels, KNCK3 expression is also observed in cardiac cell types (not shown). Finally, The nferX Signals application identifies literature evidence that both KCNK3 and KCNA5 impact cardiac electrophysiology (FIG. 14).

We further investigated the non-ion channel genes that were identified by nferX Human Genetics as containing mutations that are associated with pulmonary hypertension. Eight of these genes, CAV1, SMAD4, GJB2, ACVRL1, SMAD4, BMPR1A, BMPR2, and TBX4, have prominent associations to relevant terms associated with cardiac electrophysiology, such as “electrocardiogram”, “ECG”, “cardiac action potential”, and “cardiac conduction”. The applications nferX RNA Explorer and nferX Single Cell were used to accumulate evidence linking these genes to the heart. These genes, in addition to the potassium channel genes, may serve as a part of a gene panel of at least 10 genes that further supports the diagnosis of pulmonary hypertension given a positive ECG-based predictive test.

Example 6: TimeSeriesConvolution Neural Network Architecture without Transformer Layers

A seven-layer ‘ConvolutionTransformer’ neural network architecture was designed, which receives a 12-channel ECG and outputs an indication of the presence of pulmonary hypertension as set forth above. (SeeFIG. 15).

Example 7: ‘ConvolutionTransformer’ Neural Network Architecture with Transformer Layers

In order to increase interactions across different portions of the ECG signal compared to the TimeSeriesConvolution model of Example 6, an alternative neural network architecture referred to as ‘ConvolutionTransformer’ is provided. It employed convolutional neural networks to generate fixed size encoding for smaller portions of ECG waveforms. A sequence of such generated encodings is passed to a Transformer network to generate the predictions as illustrated in the network architecture diagram ofFIG. 16. The use of 5 second crops instead of 2 second crops enabled these longer interactions.

Example 8: Data Augmentation

Data augmentation during the training phase was used to reduce the susceptibility of the neural network to overfitting. The training data set was augmented by randomly applying one of the parameters A-F below, with 40% probability during the training alone. See alsoFIG. 17(A-E).

A. Masking a portion of time in the whole signal

B. Allowing only frequencies between 0.5 to 50 Hz

C. Stretching the signal with some zoom level

D. Shifting voltages in different leads by a small voltage

E. Dropping of a frequency band with 1 and 50 Hz

F. Shuffling a small set of leads

Results of augmentation testing for the CNN with and without transformer layers are presented in Table 8.

All publications (including patents, patent applications and sequence accession numbers mentioned herein) are hereby incorporated by reference in their entirety as if each individual publication was specifically and individually indicated to be incorporated by reference. In case of conflict, the present application, including any definitions herein, will control.