Abstract: The present invention relates to methods for determining a treatment regimen beyond surgical removal of tumor tissue for node negative or node positive breast cancer patient. The method comprises measuring the levels of urokinase-type plasminogen activator (uPA) and plasminogen activator inhibitor-1 (PAI-1) in a subject, preferably a tumor; and, based upon the values, predicting the expected benefit including disease-free survival and/or overall survival for the patient without treatment (beyond the surgical removal of tumor tissue) or with a particular treatment and using that information to select a treatment regimen for the subject. High risk subject is identified by high levels of both uPA and PAI-1, high level of uPA and low level of PAI-1 or, low level of uPA and high level of PAI-1.

Abstract: The invention is directed to a method for training at least one learning-capable system comprising the steps of providing a predetermined training data set corresponding to a predetermined number of subjects comprising a predetermined input data set and a predetermined outcome data set, augmenting the input data set and/or the outcome data set, and training each learning-capable system using the augmented input data set and/or the augmented outcome data set.

Abstract: The invention concerns a method for determining competing risks for objects following an initial event based on previously measured or otherwise objectifiable training data patterns, in which several signals obtained from a learning capable system are combined in an objective function in such a way that said learning capable system is rendered capable of detecting or forecasting the underlying probabilities of each of the said competing risks.

Abstract: The invention concerns a method for determining competing risks for objects following an initial event based on previously measured or otherwise objectifiable training data patterns, in which several signals obtained from a learning capable system are combined in an objective function in such a way that said learning capable system is rendered capable of detecting or forecasting the underlying probabilities of each of the said competing risks.