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    Base plate for a foot pedal
    52.
    外观设计
    Base plate for a foot pedal 有权

    公开(公告)号：USD1001749S1

    公开(公告)日：2023-10-17

    申请号：US29734532

    申请日：2020-05-13

    Applicant: University of South Florida

    Designer： Hassan Sadeghi

    Abstract: FIG. 1 is a perspective view of a base plate for a foot pedal showing my new design.
    FIG. 2 is a front view thereof.
    FIG. 3 is a rear view thereof.
    FIG. 4 is a right-side view thereof.
    FIG. 5 is a left-side view thereof.
    FIG. 6 is a top view thereof.
    FIG. 7 is a bottom view thereof; and,
    FIG. 8 is a perspective view of my design when in use.
    The broken lines in FIGS. 1 and 6 depict portions of the base plate for a foot pedal that form no part of the claimed design.
    The foot pedal rendered in short broken lines in FIG. 8 illustrates environmental subject matter intended to assist in showing how the design appears when in use and forms no part of the claim.
    The stippling shown in the drawings illustrates a non-skid surface.

    MACHINE LEARNING BASED PATIENT SPECIFIC POST-SURGERY MORTALITY PREDICTION SYSTEM AND RELATED METHODS
    54.
    发明公开
    MACHINE LEARNING BASED PATIENT SPECIFIC POST-SURGERY MORTALITY PREDICTION SYSTEM AND RELATED METHODS 审中-公开

    公开(公告)号：US20230290515A1

    公开(公告)日：2023-09-14

    申请号：US18174471

    申请日：2023-02-24

    Applicant: UNIVERSITY OF SOUTH FLORIDA

    Inventor： Michael P. Rogers ,  Haroon Janjua ,  Paul C. Kuo ,  Abhinav Harish

    IPC: G16H50/30 ,  G06N3/0442

    CPC classification number: G16H50/30 ,  G06N3/0442

    Abstract: Methods and systems for patient-specific post-surgery mortality prediction are disclosed. The methods and systems include: receiving a plurality of pre-operative factor indications for a patient; obtaining a first trained machine learning model and an interpretable model; applying the plurality of pre-operative factor indications to the first trained machine learning model to obtain a plurality of confidence values corresponding to the plurality of pre-operative factor indications; applying the plurality of confidence values to the interpretable model to obtain a plurality of interpretation indications, the plurality of interpretation indications corresponding to a subset of the plurality of pre-operative factor indications, the plurality of interpretation indications most contributing to mortality of the patient, the plurality of interpretation indications being specific to the patient; and outputting a survival probability of the patient based on the plurality of interpretation indications. Other aspects, embodiments, and features are also claimed and described.

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