INVERSE SURROGATE MODEL DYNAMIC PHARMACOKINETIC PARAMETER ESTIMATION

    公开(公告)号:US20230207085A1

    公开(公告)日:2023-06-29

    申请号:US17560814

    申请日:2021-12-23

    CPC classification number: G16H20/10 G16H10/60

    Abstract: A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include replicating, with patient parameters, a set of patient data of a patient and conditioning said patient parameters with at least one measure from said patient. The operations may include parameterizing a pharmacokinetic model with said patient parameters and sampling said patient parameters with a constrained optimization generative adversarial network. The operations may include calculating dosage data of a pharmaceutical with said patient parameters with said constrained optimization generative adversarial network and communicating said dosage data to a user.

    Neuron model simulation
    5.
    发明授权

    公开(公告)号:US11227692B2

    公开(公告)日:2022-01-18

    申请号:US15856582

    申请日:2017-12-28

    Abstract: One or more embodiments of the present invention include a computer-implemented method for generating neuronal models for personalized drug treatment selection for a patient. The method includes receiving allelic information for at least one neurophysiological coding region of a genome of the patient, and a physiological model of a disease associated with the genome. The method further includes determining a set of ion channels correlated with the allelic information, and receiving a set of phenotypic measurement ranges associated with the ion channels from the determined set. The method further includes performing a simulation to generate multiple neuronal models comprising the set of ion channels with parameter values within the corresponding phenotypic measurement ranges, and analyzing the generated neuronal models to identify components that affect the physiological model. The method further includes selecting a drug for the patient based at least in part on the identified components.

    NEURON MODEL SIMULATION
    10.
    发明申请

    公开(公告)号:US20190206573A1

    公开(公告)日:2019-07-04

    申请号:US15856582

    申请日:2017-12-28

    CPC classification number: G16H50/80 G16H20/10

    Abstract: One or more embodiments of the present invention include a computer-implemented method for generating neuronal models for personalized drug treatment selection for a patient. The method includes receiving allelic information for at least one neurophysiological coding region of a genome of the patient, and a physiological model of a disease associated with the genome. The method further includes determining a set of ion channels correlated with the allelic information, and receiving a set of phenotypic measurement ranges associated with the ion channels from the determined set. The method further includes performing a simulation to generate multiple neuronal models comprising the set of ion channels with parameter values within the corresponding phenotypic measurement ranges, and analyzing the generated neuronal models to identify components that affect the physiological model. The method further includes selecting a drug for the patient based at least in part on the identified components.

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