Abstract: A method for generating a software-implemented module for determining a glucose value in a body fluid. A first set of input data indicative of first values measured for first and a second input parameters is provided. A second set of input data indicative of second values for the first and second input parameters is also provided. The first and second sets of input data are processed by a physiological model to determine first and second sets of glucose values, respectively, in a body fluid. Training data is determined and a set of test data different from the training data is also determined. A software-implemented machine learning model configured to determine a glucose value in a body fluid of a patient is provided and is trained by the training data and is tested by the test data.

Abstract: Aspects of the present invention disclose a method for verifying labels of records of a dataset. The records comprise sample data and a related label out of a plurality of labels. The method includes one or more processors dividing the dataset into a training dataset comprising records relating to a selected label and an inference dataset comprising records with sample data relating to the selected label and all other labels out of the plurality of labels. The method further includes dividing the training dataset into a plurality of learner training datasets that comprise at least one sample relating to the selected label. The method further includes training a plurality of label-specific few-shot learners with one of the learner training datasets. The method further includes performing inference by the plurality of trained label-specific few-shot learners on the inference dataset to generate a plurality of sets of predicted label output values.

Abstract: Aspects of the present invention disclose a method for verifying labels of records of a dataset. The records comprise sample data and a related label out of a plurality of labels. The method includes one or more processors dividing the dataset into a training dataset comprising records relating to a selected label and an inference dataset comprising records with sample data relating to the selected label and all other labels out of the plurality of labels. The method further includes dividing the training dataset into a plurality of learner training datasets that comprise at least one sample relating to the selected label. The method further includes training a plurality of label-specific few-shot learners with one of the learner training datasets. The method further includes performing inference by the plurality of trained label-specific few-shot learners on the inference dataset to generate a plurality of sets of predicted label output values.