Ge Precision Healthcare LLC

The General Hospital Corporation

The Brigham and Women's Hospital, Inc.

Mass General Brigham Incorporated(b)

Rohit Pardasani

Hariharan Ravishankar

Abhijit Vishwas Patil

Quanzheng Li

John Michael Jordan

Siddharth Ajith

Renee L Vitullo

Hrithik Auchar

Dirk Johannes Varelmann

Pankaj Sarin

Marcio Aloisio Bezerra Cavalcanti Rockenbach

Systems and techniques for monitoring, predicting and/or alerting for short-term oxygen support needs of patients are presented. A system can include a data collection component that receives multimodal patient data for a patient having a respiratory condition in association with monitoring and treating the respiratory condition in real-time, the multimodal patient data comprising at least physiological data regarding physiological parameters tracked for the patient over a period of time, and current oxygen support data regarding a current oxygen support mechanism of the patient. The system can further include an oxygen support forecasting component that processes the multimodal patient data using an oxygen support forecasting model to generate an output forecast that indicates whether a change to the current oxygen support mechanism is recommended for the patient within a defined upcoming timeframe.

Monitoring, predicting and alerting short-term oxygen support needs for patients

Methods and systems are provided for generating respiratory support recommendations. In one embodiment, a method includes extracting imaging features from patient imaging information for a patient, extracting non-imaging features from patient clinical data of the patient, entering the imaging features and the non-imaging features to a joint model trained to output respiratory support recommendations as a function of the imaging features and the non-imaging features, and displaying one or more respiratory support recommendations output by the joint model.

Systems and methods for respiratory support recommendations

Techniques are described for performing fetal heart rate (FHR) analytics using machine learning techniques. According to an embodiment, computer-implemented method comprises training a machine learning model using a supervised machine learning process to identify patterns in training cardiotocograph data that correspond to defined physiological events associated with respective fetuses and mothers of the fetuses represented in the training cardiotocograph data. The method further comprises receiving new cardiotocograph data for a fetus and mother in real-time over a period of labor and applying the machine learning model to the new cardiotocograph data as it is received to identify the patterns in the new cardiotocograph data.

Deep learning based fetal heart rate analytics

Automated assessment for a fetus may be applied based on imaging data obtained during medical imaging examination of the fetus, with the applying including processing imaging data corresponding to a plurality of a cross-section imaging slices corresponding to a limb of the fetus, where the processing includes for each imaging slice: automatically generating a predicted outer mask for an outer contour of the limb based on application of a first pre-trained model to imaging data corresponding to the imaging slice; and automatically generating a segmentation of fat-lean mask for the imaging slice based on application of a second pre-trained model to both of the imaging data corresponding to the imaging slice and the generated predicted output mask; and applying based on the processing of the imaging data corresponding to the plurality of a cross-section imaging slices: a fractional limb volume assessment; and a fat-lean mass assessment.

Methods and systems for automatic assessment of fractional limb volume and fat lean mass from fetal ultrasound scans

Growing community of inventors

Bengaluru, India

Rohit Pardasani