A healthcare analysis system comprising: a recurrent neural network system configured to process a temporal sequence of health events associated with a patient to generate a network output for the temporal sequence, wherein processing the temporal sequence of health events comprises processing a respective input at each of a plurality of time steps, wherein the health events identified in the temporal sequence comprise a symptom or diagnosis, and wherein the health events identified in the temporal sequence further comprise: activity data identifying distances travelled by the patient, or health-related images, the recurrent neural network system comprising: one or more recurrent neural network layers and one or more logistic regression nodes, 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; 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, and a healthcare analysis engine configured to receive the network output for the temporal sequence and to generate health analysis data that characterizes the future condition scores for the predetermined set of conditions. a recurrent neural network system configured to process a temporal sequence of health events associated with a patient to generate a network output for the temporal sequence, wherein processing the temporal sequence of health events comprises processing a respective input at each of a plurality of time steps, wherein the health events identified in the temporal sequence comprise a symptom or diagnosis, and wherein the health events identified in the temporal sequence further comprise: activity data identifying distances travelled by the patient, or health-related images, activity data identifying distances travelled by the patient, or health-related images, the recurrent neural network system comprising: one or more recurrent neural network layers and one or more logistic regression nodes, 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; 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, and one or more recurrent neural network layers and one or more logistic regression nodes, 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; 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; 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, and 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, and a healthcare analysis engine configured to receive the network output for the temporal sequence and to generate health analysis data that characterizes the future condition scores for the predetermined set of conditions.