Patent ID: 11954610
Assignee: GE PRECISION HEALTHCARE LLC
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 23:
24. A method, comprising:
receiving, by a system operatively coupled to a processor, performance metrics for at least one primary machine learning model deployed in at least one site wherein the at least one primary machine learning model are trained based on respective training datasets associated with respective source domains;
generating, by the system, a surveillance dashboard that presents the performance metrics; and
in response to a determination that the performance metrics for a primary machine learning model of the at least one primary machine learning model do not meet a performance criterion, updating, by the system, the primary machine learning model, wherein the updating comprises:
processing data samples received in a target domain associated with the primary machine learning model that is different from the source domain, wherein the processing comprises, iteratively:
receiving a data sample of the data samples in the target domain;
determining, using a scope machine learning model, a confidence score for the data sample representative of a degree of confidence that a primary machine learning (ML) model will generate an accurate inference based on the data sample, wherein the primary ML model was trained on a training dataset associated with a source domain different from the target domain, wherein the training dataset does not comprise the data samples received in the target domain;
in response to determining that the confidence score meets a threshold score, employing the primary ML model to generate an inference based on the data sample; and
in response to determining that the confidence score does not meet the threshold score:
adding the data sample to a new training dataset associated with the target domain, and
in response to the new training dataset satisfying a defined criterion, training the primary ML model on the new training dataset to generate the inferences, resulting in a domain adapted version of the primary ML model.