Patents by Inventor Daniel Francis Taylor

Daniel Francis Taylor has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11961621
    Abstract: 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.
    Type: Grant
    Filed: February 10, 2023
    Date of Patent: April 16, 2024
    Assignee: REGENTS OF THE UNIVERSITY OF MICHIGAN
    Inventors: Christopher Elliot Gillies, Daniel Francis Taylor, Kevin R. Ward, Fadi Islim, Richard Medlin
  • Publication number: 20230230680
    Abstract: 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.
    Type: Application
    Filed: March 7, 2023
    Publication date: July 20, 2023
    Inventors: Kevin Ward, Daniel Francis Taylor, Michael W. Sjoding, Christopher Elliot Gillies
  • Publication number: 20230197281
    Abstract: 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.
    Type: Application
    Filed: February 10, 2023
    Publication date: June 22, 2023
    Inventors: Christopher Elliot Gillies, Daniel Francis Taylor, Kevin R. Ward, Fadi Islim, Richard Medlin
  • Patent number: 11651850
    Abstract: 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.
    Type: Grant
    Filed: October 28, 2020
    Date of Patent: May 16, 2023
    Assignee: THE REGENTS OF THE UNIVERSITY OF MICHIGAN
    Inventors: Kevin Ward, Daniel Francis Taylor, Michael W. Sjoding, Christopher Elliot Gillies
  • Patent number: 11587677
    Abstract: 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.
    Type: Grant
    Filed: November 21, 2019
    Date of Patent: February 21, 2023
    Assignee: THE REGENTS OF THE UNIVERSITY OF MICHIGAN
    Inventors: Christopher Elliot Gillies, Daniel Francis Taylor, Kevin R. Ward, Fadi Islim, Richard Medlin
  • Publication number: 20210192727
    Abstract: 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.
    Type: Application
    Filed: October 28, 2020
    Publication date: June 24, 2021
    Inventors: Kevin Ward, Daniel Francis Taylor, Michael W. Sjoding, Christopher Elliot Gillies
  • Publication number: 20200160998
    Abstract: 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.
    Type: Application
    Filed: November 21, 2019
    Publication date: May 21, 2020
    Inventors: Kevin R. Ward, Daniel Francis Taylor, Christopher Elliot Gillies