Patents by Inventor Fadi Islim

Fadi Islim 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: 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: 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