公开(公告)号：US11954610B2

公开(公告)日：2024-04-09

申请号：US16944762

申请日：2020-07-31

Applicant: GE Precision Healthcare LLC

Inventor： Junpyo Hong ,  Venkata Ratnam Saripalli ,  Gopal B. Avinash ,  Karley Marty Yoder ,  Keith Bigelow

IPC: G06N5/04 ,  G06N20/00

CPC classification number: G06N5/04 ,  G06N20/00

Abstract: Techniques are described for performing active surveillance and learning for machine learning (ML) model authoring and deployment workflows. In an embodiment, a method comprises applying, by a system comprising a processor, a primary ML model trained on a training dataset to data samples excluded from the training dataset to generate inferences based on the data samples. The method further comprises employing, by the system, one or more active surveillance techniques to regulate performance of the primary ML model in association with the applying, wherein the one or more active surveillance techniques comprise at least one of, performing a model scope evaluation of the primary ML model relative to the data samples or using a domain adapted version of the primary ML model to generate the inferences.

公开(公告)号：US20210042643A1

公开(公告)日：2021-02-11

申请号：US16944762

申请日：2020-07-31

Applicant: GE Precision Healthcare LLC

Inventor： Junpyo Hong ,  Venkata Ratnam Saripalli ,  Gopal B. Avinash ,  Karley Marty Yoder ,  Keith Bigelow

IPC: G06N5/04 ,  G06N20/00

Abstract: Techniques are described for performing active surveillance and learning for machine learning (ML) model authoring and deployment workflows. In an embodiment, a method comprises applying, by a system comprising a processor, a primary ML model trained on a training dataset to data samples excluded from the training dataset to generate inferences based on the data samples. The method further comprises employing, by the system, one or more active surveillance techniques to regulate performance of the primary ML model in association with the applying, wherein the one or more active surveillance techniques comprise at least one of, performing a model scope evaluation of the primary ML model relative to the data samples or using a domain adapted version of the primary ML model to generate the inferences.