1. A method comprising: obtaining a plurality of initial temporal sequences of health events, wherein each of the plurality of initial temporal sequences of health events is associated with a different patient, wherein each of the initial temporal sequences comprises respective health-related data associated with the respective patient that is associated with the initial temporal sequence at each of a plurality of time steps, and wherein, for one or more of the time steps in each of the initial temporal sequences, the health-related data at the time step is a respective token from a predetermined vocabulary of tokens, each token in the vocabulary representing a different health event; processing each of the plurality of initial temporal sequences of health events using a recurrent neural network to generate, for each of the initial temporal sequences, a respective network internal state of the recurrent neural network for each time step in the initial temporal sequence, wherein the recurrent neural network has been trained to receive input temporal sequences and, for each time step in each input temporal sequence, generate a network internal state for the time step and predict future events occurring after the health event identified at the time step from the network internal state for the time step; storing, for each of the plurality of initial temporal sequences, one or more of the network internal states for the time steps in the temporal sequence in an internal state repository; obtaining a first temporal sequence of health events that is associated with a current patient; processing the first temporal sequence of health events using the recurrent neural network to generate a sequence internal state for the first temporal sequence; and comparing the sequence internal state for the first temporal sequence to the network internal states for the initial temporal sequences that are stored in the internal state repository to determine a plurality of network internal states from the network internal states for the initial temporal sequences that are similar to the sequence internal state; selecting, as temporal sequences that are likely to include health events that are predictive of future health events that may become associated with the current patient, the initial temporal sequences corresponding to the similar network internal states.