公开(公告)号：US20210038166A1

公开(公告)日：2021-02-11

申请号：US16985375

申请日：2020-08-05

Applicant: Yeda Research and Development Co. Ltd.

Inventor： Eran SEGAL ,  Smadar SHILO ,  Hagai ROSSMAN

IPC: A61B5/00 ,  A61B5/145 ,  G06N20/00 ,  G16H10/60 ,  G16H10/20

Abstract: A method of predicting likelihood for childhood obesity, comprises: obtaining a plurality of parameters, wherein at least a few of the parameters characterize an infant or toddler subject. A machine learning procedure trained for predicting likelihoods for childhood obesity is feed with the plurality of parameters. An output indicative of a likelihood that the infant or toddler subject is expected to develop childhood obesity is received from the procedure. The output is related non-linearly to the parameters.

公开(公告)号：US20230076246A1

公开(公告)日：2023-03-09

申请号：US17800245

申请日：2020-02-17

Applicant: Yeda Research and Development Co. Ltd. ,  Mor Research Applications Ltd.

Inventor： Eran SEGAL ,  Smadar SHILO ,  Yotam AMAR

IPC: A61B5/00 ,  G06N3/08 ,  A61B5/145

Abstract: A method of predicting an analyte level comprises receiving a time-ordered series of levels of the analyte, monitored over a time-period; feeding a trained neural network procedure with the monitored levels; and displaying, based on an output received from the procedure, a predicted level of the analyte in a future time. The procedure can comprise a plurality of layers, wherein for at least one pair of layers, a number of inter-layer connections within the pair is higher for later monitored levels than for earlier monitored levels.

公开(公告)号：US20220328185A1

公开(公告)日：2022-10-13

申请号：US17614024

申请日：2020-05-24

Applicant: Yeda Research and Development Co. Ltd.

Inventor： Eran SEGAL ,  Smadar SHILO ,  Nitzan ARTZI

IPC: G16H50/20 ,  G16H50/30 ,  G16H10/60 ,  G16H50/70 ,  G16H10/20

Abstract: A method of predicting likelihood for gestational diabetes, comprises: obtaining a plurality of parameters characterizing a female subject, accessing a computer readable medium storing a machine learning procedure trained for predicting likelihoods for gestational diabetes, feeding the procedure with the plurality of parameters, and receiving from the procedure an output indicative of a likelihood that the subject has, or expected to develop, gestational diabetes, wherein the output indicative is related non-linearly to the parameters.