Abstract: A system for training machine learning models with unlabeled electrocardiogram signals, the system including a memory containing instructions configurating a processor to receive a plurality of electrocardiogram (ECG) data in a textual format, create one or more overlapping temporal patches from the plurality of ECG data, mask at least one temporal patch from the one or more overlapping temporal patches, pretrain an ECG machine learning model to predict the at least one masked temporal patch from the one or more overlapping temporal patches, adjust one or more parameter values of the ECG machine learning model as a function of the at least one predicted masked temporal patch and the at least one masked temporal patch and train the ECG machine learning model as a function of the one or more parameter values and a labeled set of ECG training data.

Abstract: An apparatus and method for generating a quality diagnostic of analog ECG (electrocardiogram) data. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive ECG data, extract a plurality of ECG parameters from the ECG data, convert the ECG data to one or more digitized ECG signals, validate the digitized ECG signals, wherein validating includes classifying the plurality of digitized ECG signals to a plurality of preliminary ECG parameters and determining an accuracy status of the extracted plurality of ECG parameters by comparing the plurality of preliminary ECG parameters to the extracted plurality of ECG parameters, and generate a quality diagnostic of the ECG data based on validation of the digitized ECG signals.

Abstract: An apparatus for standardization of electrocardiogram signal images, the apparatus having an imaging device, at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to receive an overlay image from the imaging device, identify a captured fixed background image and a captured primary image within the overlay image, wherein the captured primary image includes a plurality of electrocardiogram signals, compare the captured fixed background image to one or more image quality thresholds, determine an image quality score of the primary image as a function of the captured fixed background image, and output one or more image modification datum as a function of the image quality score.

Abstract: An apparatus and method for unpaired time series to time series translation is disclosed. The apparatus comprises at least a processor configured to receive an automated analysis of a time series, convert that time series from its initial domain to a usable time series within another user-selected domain, then to validate the conversion against a confidence threshold.

Abstract: An apparatus and method for detecting a level of cardiovascular disease. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive a plurality of voltage-time data, generate at least a feature vector from the voltage-time data by at least a feature model, input the at least feature vector into a cardiovascular classification model, generate at least a disease indication in a subject using the classification model, wherein the disease indication comprises a level of myocarditis, and display the at least a disease indication.

Abstract: Disclosed systems, methods, and computer readable media can diagnose a health condition based on patient time series data. For example, a method for diagnosing a health condition based on patient time series data includes identifying a training set of health records comprising a first set of patient time series data, training a neural network using the training set of health records, and executing the trained neural network model to diagnose a health condition based on a second set of patient time series data. In further examples, the first set of patient time series data and the second set of patient time series data can each comprise electrocardiogram data and the health condition can comprise pulmonary hypertension.

Abstract: Systems and methods for populating a structure database including accessing an image representation of a data table comprising one or more cells arranged in rows and columns; providing the image representation as an input to a neural network model; executing the neural network model to identify a location of the first content object in the image representation; identifying a location of the first cell based on the location of the first content object; determining that the first cell belongs to the first row and the first column based on the location of the first cell and the first content object in relation to a plurality of content objects; associating the first content object with one or more categorical identifiers; and populating a structured database with the first content object and the one or more categorical identifiers.

Abstract: An apparatus and method for populating a structured database based on an image representation of a data table, wherein the apparatus includes a processor and a memory containing instructions configuring the processor to receive an image representation having pixel data representing a data table, extract a plurality of content objects comprising at least a graphical sequence object from the data table as a function of the pixel data, wherein extracting the plurality of content objects includes identifying a content object location for each content object using a neural network model and identifying a plurality of cell locations based on the content object locations, extract sequence information associated with the at least a graphical sequence object, and populate a structured database with the plurality of content objects as a function of the sequence information and the plurality of cell locations.

Abstract: An apparatus for predicting a physiological indicator is disclosed. The apparatus comprises a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a time series input from a user. The memory instructs the processor to predict a physiological indicator as a function of the time series input using a prediction machine learning model. The memory instructs the processor to generate an uncertainty metric as a function of the physiological indicator. Wherein determining the physiological indicator includes receiving a plurality of prediction training data. Determining the physiological indicator includes fitting the plurality of prediction training data with a kernel density estimate. Determining the physiological indicator additionally includes training the prediction machine learning model using a plurality of prediction training data.

Abstract: Disclosed systems, methods, and computer readable media can diagnose a health condition based on patient time series data. For example, a method for diagnosing a health condition based on patient time series data includes identifying a training set of health records comprising a first set of patient time series data, training a neural network using the training set of health records, and executing the trained neural network model to diagnose a health condition based on a second set of patient time series data. In further examples, the first set of patient time series data and the second set of patient time series data can each comprise electrocardiogram data and the health condition can comprise pulmonary hypertension.

Abstract: Disclosed systems, methods, and computer readable media can diagnose a health condition based on patient time series data. For example, a method for diagnosing a health condition based on patient time series data includes identifying a training set of health records comprising a first set of patient time series data, training a neural network using the training set of health records, and executing the trained neural network model to diagnose a health condition based on a second set of patient time series data. In further examples, the first set of patient time series data and the second set of patient time series data can each comprise electrocardiogram data and the health condition can comprise pulmonary hypertension.

Abstract: Disclosed systems, methods, and computer readable media can diagnose a health condition based on patient time series data. For example, a method for diagnosing a health condition based on patient time series data includes identifying a training set of health records comprising a first set of patient time series data, training a neural network using the training set of health records, and executing the trained neural network model to diagnose a health condition based on a second set of patient time series data. In further examples, the first set of patient time series data and the second set of patient time series data can each comprise electrocardiogram data and the health condition can comprise pulmonary hypertension.

Abstract: Systems and methods for populating a structure database including accessing an image representation of a data table comprising one or more cells arranged in rows and columns; providing the image representation as an input to a neural network model; executing the neural network model to identify a location of the first content object in the image representation; identifying a location of the first cell based on the location of the first content object; determining that the first cell belongs to the first row and the first column based on the location of the first cell and the first content object in relation to a plurality of content objects; associating the first content object with one or more categorical identifiers; and populating a structured database with the first content object and the one or more categorical identifiers.

Abstract: The present disclosure is directed to a web services architecture comprising a component operable for rapid development of a web services application, a mobile application. The web services architecture also includes a telecommunications provider system and a service provider. The mobile application is related to the web services application deployable on a mobile device. The telecommunications provider system is operable to host the web services application and to identify the mobile device and federate information related to the mobile device. The service provider is operable to provide a mobile device service associated with the web services application. The service provider is in communication with the web services application and operable to use the federated information related to the mobile device to provide application services to the mobile device.

Abstract: A method is provided for mobile device access to back office data store. A data request is received from a mobile device, wherein the data request is associated with a really simple syndication format file link to a data file in a back office data store. The data request is conveyed through a firewall to the back office data store, wherein the firewall permits only conveying the data request. The data file is accessed in the back office data store. The data from the data file is formatted for the mobile device. The formatted data is communicated to the mobile device.

Abstract: Disclosed herein are a number of embodiments that enable BREW handsets and other devices to leverage a MSOA developed for J2ME handsets. The MSOA may be used to develop and deploy applications and services to handsets or other devices. In order to gain use of the MSOA, an actor may be developed for enabling communication with one or more servlets or one or more applications in the MSOA. Similarly, other devices, such as a set top box, may also gain the use of the MSOA through an adaptor that is capable of communication with one or more servlets or one or more applications in the MSOA. BREW handsets may also gain use of the MSOA through an actor developed in conjunction with an application as part of a servlet, midlet, actor triplet.

Abstract: The present disclosure is directed to a web services architecture comprising a component operable for rapid development of a web services application, a mobile application. The web services architecture also includes a telecommunications provider system and a service provider. The mobile application is related to the web services application deployable on a mobile device. The telecommunications provider system is operable to host the web services application and to identify the mobile device and federate information related to the mobile device. The service provider is operable to provide a mobile device service associated with the web services application. The service provider is in communication with the web services application and operable to use the federated information related to the mobile device to provide application services to the mobile device.