Predicting likelihoods of conditions being satisfied using recurrent neural networks

A recurrent neural network system configured to process a temporal sequence comprising a respective input at each of a plurality of time steps, the recurrent neural network system comprising:

• one or more recurrent neural network layers, wherein the one or more recurrent neural network layers are configured to, for each of the plurality of time steps:
• receive the input at the time step;
• and
• collectively process the input at the time step to generate a network internal state for the time step;
• one or more logistic regression nodes, wherein each of the logistic regression nodes corresponds to a respective condition from a predetermined set of conditions, and wherein each of the logistic regression nodes is configured to, for each of the plurality of time steps:
• receive the network internal state for the time step;
• and
• process the network internal state for the time step in accordance with current values of a set of parameters of the logistic regression node to generate a future condition score for the corresponding condition for the time step, wherein the future condition score for the corresponding condition represents a likelihood that the corresponding condition will be satisfied within a specified time period of the input at the time step.