1. Method for predicting changes of physiological/pathological states in a patient, based on sampling, processing and analyzing a plurality of aggregated noisy biomedical signals, comprising: a) generating a reference database of raw data streams or features, derived from said raw data streams, representing physiological/pathological states, by aggregating one or more raw data streams, each of which consisting of biomedical signals of a plurality of patients, at least several of which having one or more of said physiological/pathological states, wherein said features are obtained by performing, for each type of biomedical signal, adaptive segmentation of its corresponding raw data streams, and assigning, to each segment, individual attributes being represented by attribute values, thereby obtaining data related to each physiological/pathological states; b) generating additional data streams using said attributes. c) Repetition of stages a),b) if needed. d) determining an attribute domain, in which each segment being represented by a point that corresponds to the attribute values of said segment; e) for each physiological/pathological state, generating a set of clusters in said attribute domain, each of which consisting of a combination of points determined by their relative location to other points, by assigning a set of property values to each point, each property value corresponding to the degree of association of said point with one of the clusters; f) associating each point, in time, to a corresponding state; g) determining the probabilities of transitions between states by obtaining the frequency and the order of appearance of each point, in time; h) repeating steps d) to f) above while in each time, varying the combination of points included in each cluster according to their most updated property value and by including points derived from said probability until said updated property values remain essentially unchanged, thereby updating each cluster and said probabilities of transitions; i) generating prior knowledge data, consisting of a plurality of feasible paths between states according to said probabilities of transitions, by associating each feasible path with a corresponding dynamics of transitions between physiological/pathological states; j) associating at least one updated cluster with a normal/abnormal physiological state of said patient by using former knowledge, regarding normal/abnormal physiological/pathological states; k) For each patient, l) aggregating one or more individual data streams or features, derived from said individual data streams, each of which consisting of biomedical signals of said patient, wherein said features are obtained by performing, for each type of biomedical signal, adaptive segmentation of its corresponding raw data streams, and assigning, to each segment, individual attributes being represented by attribute values; m) Forming additional data streams out of said attributes. n) Repetition of stages k),l) if needed. o) assigning each individual attribute to a corresponding state, or to a new state, according to the probability to belong to each existing cluster or to a new cluster associated with said new or existing state and said probabilities of transitions; p) adaptively updating each existing or new cluster and said probabilities of transitions according to said individual data streams; q) obtaining a path, being an individual dynamics, between physiological/pathological states according to their order of appearance; and r) obtaining a prediction of being in, or transitions to, physiological/pathological states in said patient, by comparing said individual dynamics with known dynamics, obtained from prior knowledge.