Abstract: A method comprises obtaining data characterizing a given subject over time, applying at least a portion of the obtained data to a generative adversarial network adapted to generate a prediction of at least one change in at least one of behavior and physiology of the given subject from the obtained data, and executing at least one automated remedial action relating to the given subject based at least in part on the generated prediction. The generative adversarial network is configured to implement multi-task learning, across a plurality of subjects, in which changes in multiple distinct features are treated as separate but linked tasks. The generative adversarial network comprises separate discriminators for each of the multiple distinct features and separate discriminators for each of a plurality of different clusters of respective subsets of the plurality of subjects, and combines outputs of respective ones of the discriminators for the features and the clusters in generating the prediction.

Abstract: A method in an illustrative embodiment comprises obtaining data characterizing a given subject over time, applying at least a portion of the obtained data to an encoder-decoder neural network adapted to generate a prediction of at least one change in at least one of behavior and physiology of the given subject from the obtained data, and executing at least one automated remedial action relating to the given subject based at least in part on the generated prediction. The encoder-decoder neural network is configured to learn one or more subject-specific anomaly thresholds based at least in part on reconstruction error of the encoder-decoder neural network. The encoder-decoder neural network is illustratively implemented utilizing at least one of a fully-connected neural network autoencoder architecture and a gated recurrent unit sequence-to-sequence architecture. Other illustrative embodiments include systems and computer program products.