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    EXECUTION AND COMMUNICATION PROTOCOL FOR ALGORITHMIC PROCESSING IN A DIAGNOSTICS SYSTEM
    3.
    发明公开
    EXECUTION AND COMMUNICATION PROTOCOL FOR ALGORITHMIC PROCESSING IN A DIAGNOSTICS SYSTEM 审中-公开

    公开(公告)号：US20240079086A1

    公开(公告)日：2024-03-07

    申请号：US17902558

    申请日：2022-09-02

    Applicant: TEMPUS LABS, INC.

    Inventor： Charles Jaros ,  Nayeem Qadir ,  Isaiah Simpson ,  Marc Wielansky

    IPC: G16B20/00 ,  G16B40/20

    CPC classification number: G16B20/00 ,  G16B40/20 ,  G16H50/30

    Abstract: The following relates generally to determining genomic biomarkers from biological data (e.g., a read of a nucleic acid, or an image, such as an image of a slide). In some embodiments, a transform orchestrator: (i) receives an order to transform biological data to one or more genomic biomarkers; (ii) selects a transform for deriving each of the received one or more genomic biomarkers; (iii) associates each selected transform with a cloud computing platform; (iv) executes instructions for each selected transform; (v) communicates an operational status of each selected transform; (vi) stores the genomic biomarker output from each selected transform; and (vii) provides a notification of a final operational status of each selected transform.

    Generalizable and interpretable deep learning framework for predicting MSI from histopathology slide images
    7.
    发明授权
    Generalizable and interpretable deep learning framework for predicting MSI from histopathology slide images 有权

    公开(公告)号：US11741365B2

    公开(公告)日：2023-08-29

    申请号：US16412362

    申请日：2019-05-14

    Applicant: TEMPUS LABS, INC.

    Inventor： Aly Azeem Khan

    IPC: G06N5/02 ,  G06N3/084 ,  G06N20/10

    CPC classification number: G06N3/084 ,  G06N5/02 ,  G06N20/10

    Abstract: A generalizable and interpretable deep learning model for predicting microsatellite instability from histopathology slide images is provided. Microsatellite instability (MSI) is an important genomic phenotype that can direct clinical treatment decisions, especially in the context of cancer immunotherapies. A deep learning framework is provided to predict MSI from histopathology images, to improve the generalizability of the predictive model using adversarial training to new domains, such as on new data sources or tumor types, and to provide techniques to visually interpret the topological and morphological features that influence the MSI predictions.

    Evaluating effect of event on condition using propensity scoring
    8.
    发明授权
    Evaluating effect of event on condition using propensity scoring 有权

    公开(公告)号：US11715565B2

    公开(公告)日：2023-08-01

    申请号：US16679054

    申请日：2019-11-08

    Applicant: Tempus Labs, Inc.

    Inventor： Ashraf Hafez ,  Caroline Epstein

    IPC: G16H50/30 ,  G06F3/01 ,  G16H50/70

    CPC classification number: G16H50/30 ,  G06F3/011 ,  G16H50/70

    Abstract: Systems and methods are provided for implementing a tool for evaluating an effect on an event, such as a medication or treatment, on a subject's condition, using a propensity model that identifies matched treatment and control cohorts within a base population of subjects. A propensity value threshold, which can be obtained based on user input, can be used to adjust the selection of subjects for treatment and control cohorts. The tool allows analyzing features of the subjects in the treatment and control groups, and further allows for evaluation and comparison of survival objectives of subjects in the treatment and control groups.

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