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: The disclosure relates to a method for screening a subject for the risk of chronic kidney disease (CKD), comprising: receiving marker data indicative for a plurality of marker parameters for a subject, such plurality of marker parameters indicating, for the subject for a measurement period, an age value, a sample level of creatinine, and a sample level of albumin; and determining a risk factor indicative of the risk of suffering CKD for the subject from the plurality of marker parameters, wherein the determining comprises: weighting the age value higher than the sample level of albumin, and weighting the sample level of creatinine higher than the sample level of albumin. Further, a computer-implemented method for screening a subject and a method for screening a subject for the risk of chronic kidney disease (CKD) are provided.