University of Michigan

Gm Global Technolgoy Operations, Inc.

Daniel Taylor

Kevin R Ward

Christopher Elliot Gillies

Richard C Medlin

Fadi Islim

Michael W Sjoding

Orhan Bulan

Scott D Schwab

Xinggao Fang

David Hahn Clifford

Andrew J Lingg

Sean Douglas Vermillion

Matthew Kelly Titsworth

Benjamin J Cool

Elyas Sabeti

In exemplary embodiments, methods and systems are provided that include: obtaining, via telematics systems of a plurality of vehicles, vehicle telemetry data as the plurality of vehicles travel through one or more geographic regions; transforming, from a computer processor, the vehicle telemetry data into a geohash encoded format pertaining to the one or more geographic regions; identifying, via a processor, a plurality of road junctions using the vehicle telemetry data that is transformed into a geohashed encoding; identifying, via the processor, a plurality of road segments using the vehicle telemetry data that is transformed into a geohashed encoding for the one or more geographic regions; and generating, via the processor, a road network mapping for the one or more geographic regions that includes the plurality of road junctions and the plurality of road segments utilizing the vehicle telemetry data that is transformed into a geohashed encoding.

Road network mapping using vehicle telemetry data

A computing system includes a processor; and a memory having stored thereon an adjustment application comprising computer-executable instructions that, when executed, cause the computing system to: display a graphical user interface including a digital medical image of a patient; superimpose a bounding box; receive an adjustment of an area of interest; and provide an adjusted digital medical image. A non-transitory computer-readable medium includes computer-executable instructions that, when executed via one or more processors, cause a computer to: display a graphical user interface including a digital medical image of a patient; superimpose a bounding box; receive an adjustment of an area of interest; and provide an adjusted digital medical image. A computer-implemented method includes: displaying a graphical user interface including a digital medical image of a patient; superimposing a bounding box; receiving an adjustment of an area of interest; and providing an adjusted digital medical image.

Computer vision technologies for rapid detection

A method includes receiving patient health data; determining a score using a trained machine learning model; determining a threshold value using an adaptive threshold tuning learning model; comparing the score to the threshold value; and generating an alarm. A computing system includes a processor; and a memory having stored thereon instructions that, when executed by the processor, cause the computing system to: receive patient health data; determine a score using a trained machine learning model; determine a threshold value using an adaptive threshold tuning learning model; compare the score to the threshold value; and generate an alarm. A non-transitory computer readable medium includes program instructions that when executed, cause a computer to: receive patient health data; determine a score using a trained machine learning model; determine a threshold value using an adaptive threshold tuning learning model; compare the score to the threshold value; and generate an alarm.

Predicting intensive care transfers and other unforeseen events using machine learning

A computer-implemented method includes preprocessing a variable dimension medical image, identifying one or more areas of interest in the medical image; and analyzing the one or more areas of interest using a deep learning model. A computing system includes one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the computing system to preprocess a variable dimension medical image, identify one or more areas of interest in the medical image; and analyze the one or more areas of interest using a deep learning model. A non-transitory computer readable medium contains program instructions that when executed, cause a computer to preprocess a variable dimension medical image, identify one or more areas of interest in the medical image, and analyze the one or more areas of interest using a deep learning model.

A method of predicting patient deterioration includes receiving an electronic health record data set of the patient, determining a risk score corresponding to the patient by analyzing the electronic health record data set of the patient using a trained machine learning model, determining a threshold value using an online/reinforcement learning model, comparing the risk score to the threshold value, and when the risk score exceeds the threshold value, generating an alarm. A non-transitory computer readable medium includes program instructions that when executed, cause the computer to receive a list of patients, display selectable patient information corresponding to each of the list of patients according to an ordering established by a feature importance algorithm, receive a selection, retrieve vital sign information corresponding to the selection, and display the vital sign information.

Systems and methods of mapping traffic related objects. In one embodiment, a method includes: receiving image sensor data relating to an environment associated with a vehicle; receiving lidar sensor data relating to the environment associated with the vehicle; selecting, by a processor, at least one image region including a traffic related object from the image sensor data; selectively filtering, by a processor, the at least one image region based on the lidar sensor data; and generating, by a processor, a composite map including a representation of the traffic related object superimposed upon a defined map of the environment based on the selectively filtering.

Growing community of inventors

Belleville, MI, United States of America

Daniel Taylor