Ge Precision Healthcare LLC

Microsoft Technology Licensing, LLC

The General Hospital Corporation

The Brigham and Women's Hospital, Inc.

Partners Healthcare System, Inc.

Neil Tenenholtz

John Francis Kalafut

Stuart Pomerantz

Keith Dreyer

Sean Doyle

Mark Heinz Michalski

Bernardo Canedo Bizzo

Ramon Gilberto Gonzalez

Vijaykumar K Aski

Christopher P Bridge

Stefano Pedemonte

Nikolay Sergeyevich Rovinskiy

Dhawal Dilip Parkar

Nazmiye Ceren Abay

Mark Michalski

Techniques are described that facilitate integrating artificial intelligence (AI) informatics in healthcare systems using a distributed learning platform. In one embodiment, a computer-implemented is provided that comprises interfacing, by a system operatively coupled to a processor, with a medical imaging application that provides for viewing medical image data. The method further comprises, facilitating, by the system, generation of structured diagnostic data according to a defined ontology in association with usage of the imaging application to perform a clinical evaluation of the medical image data. The method further comprises providing, by the system, the structured diagnostic data to one or more machine learning systems, wherein based on the providing, the one or more machine learning systems employ the structured diagnostic data as training data to generate or train one or more diagnostic models configured to provide artificial intelligence-based diagnostic evaluations of new medical image data.

Facilitating artificial intelligence integration into systems using a distributed learning platform

The disclosed techniques pertain to the dynamic control of select functions that are applied to a time series dataset based on the detection of stationary time series grains. In some configurations, a system selectively applies select functions, e.g., the application of a differencing function, to a dataset in response to determining that a number of stationary time series grains detected in the dataset meets one or more criteria with respect to a threshold. If a system determines that the number of stationary time series grains meets one or more criteria with respect to a threshold, the system can apply a differencing function to the entire dataset. By controlling the differencing function based on the detection of stationary time series grains with respect to a threshold, the system can increase the accuracy and the efficiency of a machine learning system or any other system that utilizes time series datasets.

Dynamically controlled processing of time series datasets based on the detection of stationary time series grains

Systems and techniques for generating and/or employing a medical imaging stroke model are presented. In one example, a system employs a convolutional neural network to generate output data regarding a brain anatomical region based on diffusion-weighted imaging (DWI) data associated with the brain anatomical region and apparent diffusion coefficient (ADC) data associated with the brain anatomical region. The system also detects presence or absence of a medical stroke condition associated with the brain anatomical region based on the output data.

Medical imaging stroke model

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Cambridge, MA, United States of America

Neil Tenenholtz