Veveo, Inc.

Anumana, Inc.

Nference, Inc.

Adeia Guides Inc.

Rovi Guides, Inc.

Mayo Foundation for Medical Education and Research

Rakesh Barve

Murali Aravamudan

Sashikumar Venkataraman

Ajit Rajasekharan

Pankaj Garg

Kajamalai Gopalaswamy Ramakrishnan

Vineet Agarwal

Suthirth Vaidya

Girish S Welling

K China Srinivasulu Reddy

Sankar Ardhanari

Ahmed Nizam Mohaideen Pathurudeen

Ganesh Ramamoorthy

Ashim Prasad

Arjun Puranik

Pranav Rajanala

Animesh Agarwal

Venkataramanan Soundararajan

Keyur Faldu

Uddeshya Upadhyay

Abhijith Chunduru

Apurv Anand

Tyler Wagner

Abhijit Satchidanand Savarkar

Sai Saketh Chennamsetty

Mangesh Dattatray Pujari

Akash Anand

Soumyadip Majumder

Samir Awasthi

Manish Mohan Sharma

Sairam Bade

Melwin Babu

Shamim A Naqvi

Karthik Murugadoss

Shashi Kant

Yash Mishra

Deepak Anand

Rahul Agrawal

Daren Gill

Rohit Jain

Karthik K Bharadwaj

Yogisha Heggadahalli Jayendra

Katherine Carlson

Corinne Carpenter

Praveen Kumar M

Yash Gupta

Sughosh Indurkar

Sravan Kumar Lalam

Maulik Nanavaty

Paul A Friedman

Praveen Kumar

Itzhak Zachi Attia

Akshat Agarwal

Aravind Srinivasan

Mayank Sharma

Ravi Krishna

Shiva Verma

Peter A Noseworthy

Kirnesh Nandan

Purushottam Sinha

Durgaprasad Dodle

Hari Krishna Kunderu

Harish Kumar B V

Michiel Jm Niesen

Shayan Ghosh

Shashank Jaiswal

Keyur Vallabhbhai Faldu

Kajamalai G Ramakrishnan

Joerg Herrmann

John Rincón-Hekking

Venkataraman Soundarajan

Girish Welling

A system for training multimodal self-supervised models, the system including a computing device configured to receive a multimodal dataset encode each data entry of a plurality of data entries into a single data format to create an encoded multimodal dataset including one or more encoded modalities, identify one or more embedded pairs as a function of the one or more encoded modalities, wherein each embedded pair of the one or more embedded pairs is associated with a separate patient of the plurality of patients, map the embedded pairs within a shared embedding space, pretrain an ECG machine learning model as a function of the mapping by adjusting one or more parameter values of the ECG machine learning model, receive labeled training data and train the pretrained ECG machine learning model as a function of the labeled training data.

Systems and methods for training multimodal self-supervised models

An apparatus and method for optimizing an operation using a deviation are disclosed. The apparatus includes a memory communicatively connected to at least a processor, wherein the memory contains instructions configuring the at least a processor to receive a plurality of operation notes associated with a plurality of operators and a plurality of operations, label the plurality of operation notes to at least an operation label, identify at least an operation deviation, using a language processing module, in a portion of the plurality of operation notes having the at least a matching operation label as a function of at least an operation factor, determine a relevance datum of the at least an operation deviation, determine an effectiveness datum of the at least an operation deviation and generate modified operation data as a function of the effectiveness datum and the relevance datum of the at least an operation deviation.

Apparatus and method for optimizing an operation using a deviation

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.

Apparatus and method for unpaired time series data translation

A system and method of processing a search request is provided. Identification of a desired content item is based on comparing a topic of the search request to previous user input. The method includes providing access to a set of content items with metadata that describes the corresponding content items and providing information about previous searches. The method further includes receiving a present input from the user and determining a relatedness measure between the information about the previous searches and an element of the present input. If the relatedness measure is high, the method also includes selecting a subset of content items based on comparing the present input and information about the previous searches with the metadata that describes the subset of content items. Otherwise, the method includes selecting a subset of content items based on comparing the present input with the metadata that describes the subset of content items.

Method for adaptive conversation state management with filtering operators applied dynamically as part of a conversational interface

Apparatus and method for data analysis for unmet need identification, solution distillation and data structure generation are disclosed. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory contains instructions configuring the at least a processor to receive a plurality of sets of note data, determine a plurality of operational language elements from the plurality of sets of note data, wherein the plurality of operational language elements includes an unmet need language element, generate an element control datum as a function of the unmet need language element of the plurality of operational language elements, classify the plurality of operational language elements into one or more element groups, generate a note data structure as a function of the one or more element groups and the element control datum, and display the note data structure using a graphical user interface.

Apparatus and method for note data analysis to identify unmet needs and generation of data structures

Apparatus and method for generating pseudo-EGM data from ECG data are disclosed. 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 generate EGM model training data, wherein generating the EGM model training data includes receiving the EGM model training data, wherein the EGM model training data includes exemplary ECG data correlated to exemplary EGM data and time synchronizing the exemplary ECG data and the exemplary EGM data, train an EGM machine-learning model using the EGM model training data, receive subject data, wherein the subject data includes subject ECG data and generate subject EGM data as a function of the subject ECG data using the trained EGM machine-learning model.

Apparatus and method for generating pseudo-electrogram (EGM) data from electrocardiogram (ECG) data

Apparatus for visualization within a three-dimensional (3D) model and methods used therein are described, wherein the apparatus includes a processor and a memory communicatively connected to the processor, wherein the memory includes instructions configuring the processor to receive a query image, extract neural network encodings from the received query image, query a synthetic image repository for at least a matching synthetic image, and display an estimated position and orientation within the 3D model, wherein the synthetic image repository includes a plurality of synthetic images and their extracted neural network encodings, each synthetic image therein corresponds to a slice extracted at a specific position and orientation in the 3D model, and querying the synthetic image repository includes comparing the extracted neural network encodings between the query image and synthetic images.

Apparatus and methods for visualization within a three-dimensional model using neural networks

Apparatus and method for object pose estimation are disclosed. 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 sets of echo data, wherein the plurality of sets of echo data is configured for generation of a plurality of echo depth maps, segment the plurality of echo depth maps, determine a depth datum related to pixels of an object of interest as a function of the plurality of segmented echo depth maps, generate a three dimensional (3D) point cloud related to the object of interest as a function of the depth datum and generate a pose datum of the object of interest as a function of the 3D point cloud.

Apparatus and method for object pose estimation in a medical image

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.

Method and an apparatus for detecting a level of cardiovascular disease

System for observing medical conditions and methods used therein include a processor and a memory connected to the processor, wherein the memory contains instructions configuring the processor to receive, from a data repository, a plurality of reference electronic health records and a plurality of reference cardiac data elements, generate medical training data, train at least an observation machine learning model using the generated medical training data, receive a query pertaining to a subject, wherein the query includes at least a query cardiac data element and at least a query electronic health record, and output at least an observation outcome as a function of the query using the at least an observation machine learning model.

System and methods for observing medical conditions

A system for transforming electrocardiogram images for use in one or more machine learning models, the system including at least a processor configured to receive paired data including a plurality of ECG images correlated to a plurality of standardized images, wherein each ECG image of the plurality of ECG images is correlated with each standardized image of the plurality of standardized images, train an ECG transformation model as a function of the paired data, wherein training the transformation model includes adjusting one or more parameter values of the ECG transformation model as a function of a comparison between at least one predicted standardized image and at least one standardized image of the plurality of standardized images, receive non-conforming data, generate standardized data as a function of the ECG transformation model and the non-conforming data and train an ECG machine learning model as a function of the standardized data.

Systems and methods for transforming electrocardiogram images for use in one or more machine learning models

An apparatus and method for prediction of pulmonary vein reconnection is disclosed. The apparatus includes an electrocardiogram device, 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 generate ablation evaluation training data, train an ablation evaluation machine-learning model using the ablation evaluation training data, receive, from the electrocardiogram device, the electrocardiogram data, and generate, using the ablation evaluation machine-learning model, ablation evaluation data of the patient, wherein generating the ablation evaluation data of the patient includes inputting, into the ablation evaluation machine-learning model, the electrocardiogram data and receiving as output, from the ablation evaluation machine-learning model, the ablation evaluation data of the patient.

Apparatus and methods for prediction of repeat ablation efficacy

A system for determining position signals of electrodes using a retrained machine-learning model includes at least a catheter including a plurality of electrodes configured to collect a plurality of potential signals and a magnetic sensor configured to collect magnetic data, and at least a computing device including a memory. The processor receives a first training set, wherein the first training set includes patient-agnostic data; receives a second training set, wherein the second training set includes patient-specific data, trains a mapping machine-learning model using the first training set, retrains the mapping machine-learning model using the second training set, receives at least a first signal, wherein the first signal includes a potential signal of the plurality of potential signal and the magnetic data, and generates, using the retrained machine-learning model, as a function of the at least a first signal, a first position signal for an electrode of the plurality of electrodes.

Methods and systems for determining position signals of electrodes using a retrained machine-learning model

An apparatus (and/or method) for training machine-learning model to make determinations from mismatched channel signals, wherein the apparatus includes a processor and memory communicatively connected to the processor. The memory containing instructions configuring the processor to receive a first set of signals using a first number of channels, process the first set of signals to simulate a second set of signals from a second number of channels, train a signal conversion model using the first set of signals and the second set of signals, and output a set of converted signals having the first number of channels using the third set of signals and the trained signal conversion model.

Apparatus (and/or method) of training a machine-learning model to generate determinations using mismatched-channel signals

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Bengaluru, India

Rakesh Barve