Abstract: A method for configuring a single architecture deep learning model includes receiving parameters; configuring segments; and processing segment, combination and/or fully-connected outputs to generate modeling output. A computing system comprising: a processor; and a memory having stored thereon computer-executable instructions that, when executed, cause the computing system to: receive parameters; configure segments; and process segment, combination and/or fully-connected outputs to generate modeling output. A non-transitory computer-readable medium having stored thereon computer-executable instructions that, when executed by one or more processors, cause a computer to: receive parameters; configure segments; and process segment, combination and/or fully-connected outputs to generate modeling output.

Abstract: Systems and methods for automated real estate property matching across disparate data sources are disclosed. A method may include: retrieving first property data and second property data; normalizing the first property data and the second property data; executing a first phase of a configurable systemic multi-step matching process to match a property in the first property data to the property in the second property data, the first phase outputting a first score; executing a second phase of the configurable systemic multi-step matching process, the second phase outputting a second score; determining that the first score to the second score are within a predetermined amount indicating an exact match; populating an entry in a unified property database with matching data from the first property data and the second property data; and storing the entry for the property in the unified property database.

Abstract: A method and system for predicting the likelihood that a patient will suffer from a cardiac event is provided. The method includes receiving electrocardiogram data associated with the patient, providing at least a portion of the electrocardiogram data to a trained model, receiving a risk score indicative of the likelihood the patient will suffer from the cardiac event within a predetermined period of time from when the electrocardiogram data was generated, and outputting the risk score to at least one of a memory or a display for viewing by a medical practitioner or healthcare administrator. The system includes at least one processor executing instructions to carry out the steps of the method.

Abstract: A method for determining cardiology disease risk from electrocardiogram trace data and clinical data includes receiving electrocardiogram trace data associated with a patient, receiving the patient's clinical data, providing both sets of data to a trained machine learning composite model that is trained to evaluate the data with respect to each disease of a set of cardiology diseases including three or more of cardiac amyloidosis, aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid regurgitation, abnormal reduced ejection fraction, or abnormal interventricular septal thickness, generating, by the model and based on the evaluation, a composite risk score reflecting a likelihood of the patient being diagnosed with one or more of the cardiology diseases within a predetermined period of time from when the electrocardiogram trace data was generated, and outputting the composite risk score to at least one of a memory or a display.

Abstract: A method for determining cardiology disease risk from electrocardiogram trace data and clinical data includes receiving electrocardiogram trace data associated with a patient, receiving the patient's clinical data, providing both sets of data to a trained machine learning composite model that is trained to evaluate the data with respect to each disease of a set of cardiology diseases including three or more of cardiac amyloidosis, aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid regurgitation, abnormal reduced ejection fraction, or abnormal interventricular septal thickness, generating, by the model and based on the evaluation, a composite risk score reflecting a likelihood of the patient being diagnosed with one or more of the cardiology diseases within a predetermined period of time from when the electrocardiogram trace data was generated, and outputting the composite risk score to at least one of a memory or a display.

Abstract: A method and system for determining cardiac disease risk from electrocardiogram trace data is provided. The method includes receiving electrocardiogram trace data associated with a patient, the electrocardiogram trace data having an electrocardiogram configuration including a plurality of leads. One or more leads of the plurality of leads that are derivable from a combination of other leads of the plurality of leads are identified, and a portion of the electrocardiogram trace data does not include electrocardiogram trace data of the one or more leads. The portion of the electrocardiogram data is provided to a trained machine learning model, to evaluate the portion of the electrocardiogram trace data with respect to one or more cardiac disease states. A risk score reflecting a likelihood of the patient being diagnosed with a cardiac disease state within a predetermined period of time is generated by the trained machine learning model based on the evaluation.

Abstract: A method and system for predicting the likelihood that a patient will suffer from a cardiac event is provided. The method includes receiving electrocardiogram data associated with the patient, providing at least a portion of the electrocardiogram data to a trained model, receiving a risk score indicative of the likelihood the patient will suffer from the cardiac event within a predetermined period of time from when the electrocardiogram data was generated, and outputting the risk score to at least one of a memory or a display for viewing by a medical practitioner or healthcare administrator. The system includes at least one processor executing instructions to carry out the steps of the method.

Abstract: A method and system for predicting the likelihood that a patient will suffer from a cardiac event is provided. The method includes receiving electrocardiogram data associated with the patient, providing at least a portion of the electrocardiogram data to a trained model, receiving a risk score indicative of the likelihood the patient will suffer from the cardiac event within a predetermined period of time from when the electrocardiogram data was generated, and outputting the risk score to at least one of a memory or a display for viewing by a medical practitioner or healthcare administrator. The system includes at least one processor executing instructions to carry out the steps of the method.

Abstract: A method includes the step of receiving electrocardiogram (ECG) data associated with a plurality of patients and an electrocardiogram configuration including a plurality of leads and a time interval. The electrocardiogram data includes, for each lead included in the plurality of leads, voltage data associated with at least a portion of the time interval. The method also includes training an artificial intelligence model on the ECG data, tuning the artificial intelligence model using data from a device having fewer leads than the plurality of leads, and evaluating the artificial intelligence model on additional data received from the ECG data.

Abstract: A method for determining cardiology disease risk from electrocardiogram trace data and clinical data includes receiving electrocardiogram trace data associated with a patient, receiving the patient's clinical data, providing both sets of data to a trained machine learning composite model that is trained to evaluate the data with respect to each disease of a set of cardiology diseases including three or more of cardiac amyloidosis, aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid regurgitation, abnormal reduced ejection fraction, or abnormal interventricular septal thickness, generating, by the model and based on the evaluation, a composite risk score reflecting a likelihood of the patient being diagnosed with one or more of the cardiology diseases within a predetermined period of time from when the electrocardiogram trace data was generated, and outputting the composite risk score to at least one of a memory or a display.

Abstract: Foley type catheter embodiments for sensing physiologic data from a urinary tract of a patient are disclosed. The system includes the catheter and a data processing apparatus and methods for sensing physiologic data from the urinary tract. Embodiments may also include a pressure sensor having a pressure interface at a distal end of the catheter, a pressure transducer at a proximal end, and a fluid column disposed between the pressure interface and transducer. When the distal end is residing in the bladder, the pressure transducer can transduce pressure impinging on it into a chronological pressure profile, which can be processed by the data processing apparatus into one or more distinct physiologic pressure profiles, for example, peritoneal pressure, respiratory rate, and cardiac rate. At a sufficiently high data-sampling rate, these physiologic data may further include relative pulmonary tidal volume, cardiac output, relative cardiac output, and absolute cardiac stroke volume.

Abstract: Foley type catheter embodiments for sensing physiologic data from a urinary tract of a patient are disclosed. The system includes the catheter and a data processing apparatus and methods for sensing physiologic data from the urinary tract. Embodiments may also include a pressure sensor having a pressure interface at a distal end of the catheter, a pressure transducer at a proximal, and a fluid column disposed between the pressure interface and transducer. When the distal end is residing in the bladder, the pressure transducer can transduce pressure impinging on it into a chronological pressure profile, which can be processed by the data processing apparatus into one or more distinct physiologic pressure profiles, for example, peritoneal pressure, respiratory rate, and cardiac rate. At a sufficiently high data-sampling rate, these physiologic data may further include relative pulmonary tidal volume, cardiac output, relative cardiac output, and absolute cardiac stroke volume.

Abstract: Three-dimensional (3D) projections of nuclear magnetic resonance (NMR) signals are acquired from a liver experiencing NMR in response to a 3D multi-echo non-Cartesian pulse sequence. The projections are reconstructed into two sets of images having different resolutions. Bins associated with the different positions to which the liver moves during respiration are identified in lower resolution images, and then higher resolution images are binned into the position dependent bins based on navigator data in the lower resolution images. A combined image for a bin is made from images located in the bin and then registered to a reference image. An overall combined image is made by summing the combined bin images. Quantized data for a contrast agent concentration in the liver is produced using signal intensity in the overall combined image. The quantized value may describe a liver perfusion parameter. A diagnosis may be made from the quantized value.

Abstract: Three-dimensional (3D) projections of nuclear magnetic resonance (NMR) signals are acquired from a liver experiencing NMR in response to a 3D multi-echo non-Cartesian pulse sequence. The projections are reconstructed into two sets of images having different resolutions. Bins associated with the different positions to which the liver moves during respiration are identified in lower resolution images, and then higher resolution images are binned into the position dependent bins based on navigator data in the lower resolution images. A combined image for a bin is made from images located in the bin and then registered to a reference image. An overall combined image is made by summing the combined bin images. Quantized data for a contrast agent concentration in the liver is produced using signal intensity in the overall combined image. The quantized value may describe a liver perfusion parameter. A diagnosis may be made from the quantized value.

Abstract: Foley type catheter embodiments for sensing physiologic data from a urinary tract of a patient are disclosed. The system includes the catheter and a data processing apparatus and methods for sensing physiologic data from the urinary tract. Embodiments may also include a pressure sensor having a pressure interface at a distal end of the catheter, a pressure transducer at a proximal end, and a fluid column disposed between the pressure interface and transducer. When the distal end is residing in the bladder, the pressure transducer can transduce pressure impinging on it into a chronological pressure profile, which can be processed by the data processing apparatus into one or more distinct physiologic pressure profiles, for example, peritoneal pressure, respiratory rate, and cardiac rate. At a sufficiently high data-sampling rate, these physiologic data may further include relative pulmonary tidal volume, cardiac output, relative cardiac output, and absolute cardiac stroke volume.

Abstract: A multi-purpose child support device comprises a frame having a base for placement on a floor surface and a support section positioned above said base. A seat element is configured for receiving a toddler child in a sitting position and is coupled with the support section above the floor surface for forming a high chair. The seat element is removably mounted to the frame and is operable for being selectively removed from the support section such that the support section receives an infant child carrier for supporting an infant child carrier above a floor surface. A mounting element is positioned on the frame and engages the removed seat element for securing the removed seat element to the frame.

Abstract: A multi-purpose child support device comprises a frame having a base for placement on a floor surface and a support section positioned above said base. A seat element is configured for receiving a toddler child in a sitting position and engaging the support section above the floor surface to form a high chair. The seat element is pivotally mounted to the frame and is operable for being selectively pivoted away from the support section such that the support section receives an infant child carrier for supporting an infant child carrier above a floor surface.

Abstract: A multi-purpose child support device comprises a frame having a base for placement on a floor surface and a support section positioned above said base. A seat element is configured for receiving a toddler child in a sitting position and engaging the support section above the floor surface to form a high chair. The seat element is pivotally mounted to the frame and is operable for being selectively pivoted away from the support section such that the support section receives an infant child carrier for supporting an infant child carrier above a floor surface.

Abstract: A multi-purpose child support device comprises a frame having a base for placement on a floor surface and a support section positioned above said base. A seat element is configured for receiving a toddler child in a sitting position and is coupled with the support section above the floor surface for forming a high chair. The seat element is removably mounted to the frame and is operable for being selectively removed from the support section such that the support section receives an infant child carrier for supporting an infant child carrier above a floor surface. A mounting element is positioned on the frame and engages the removed seat element for securing the removed seat element to the frame.