A system comprising: a bed having a mattress; one or more sensors of a sensor array configured to sense one or more physical phenomena of a sleeper on the bed, generate data signals based on the sensed physical phenomena and send, to a computing system, the data signals; and a computing system comprising one or more processors and computer memory, the computing system configured to receive the data signals, generate, from data signals of a sleep session of the sleeper and by a feature extractor, a feature vector of features, each feature having a feature value that represents one of the physical phenomena, and classify, by executing a state-classifier, the sleeper into a classified physical state for the sleep session of the sleeper, wherein the state-classifier uses a prediction model that uses the feature vector to output the classified physical state based on a pre-defined plurality of possible physical states, characterized in that the generation of the feature vector comprises selecting the features for the feature vector based on historical analysis of training data, wherein the selection is made by analyzing a pool of candidate features to identify the features for the feature vector that are most predictive for a particular onset of illness, symptoms, or clinical diagnoses. a bed having a mattress; one or more sensors of a sensor array configured to sense one or more physical phenomena of a sleeper on the bed, generate data signals based on the sensed physical phenomena and send, to a computing system, the data signals; and a computing system comprising one or more processors and computer memory, the computing system configured to receive the data signals, generate, from data signals of a sleep session of the sleeper and by a feature extractor, a feature vector of features, each feature having a feature value that represents one of the physical phenomena, and classify, by executing a state-classifier, the sleeper into a classified physical state for the sleep session of the sleeper, wherein the state-classifier uses a prediction model that uses the feature vector to output the classified physical state based on a pre-defined plurality of possible physical states, characterized in that the generation of the feature vector comprises selecting the features for the feature vector based on historical analysis of training data, wherein the selection is made by analyzing a pool of candidate features to identify the features for the feature vector that are most predictive for a particular onset of illness, symptoms, or clinical diagnoses. receive the data signals, generate, from data signals of a sleep session of the sleeper and by a feature extractor, a feature vector of features, each feature having a feature value that represents one of the physical phenomena, and classify, by executing a state-classifier, the sleeper into a classified physical state for the sleep session of the sleeper, wherein the state-classifier uses a prediction model that uses the feature vector to output the classified physical state based on a pre-defined plurality of possible physical states, characterized in that the generation of the feature vector comprises selecting the features for the feature vector based on historical analysis of training data, wherein the selection is made by analyzing a pool of candidate features to identify the features for the feature vector that are most predictive for a particular onset of illness, symptoms, or clinical diagnoses.