Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting future patient health using a recurrent neural network. In particular, at each time step, a network input for the time step is processed using a recurrent neural network to update a hidden state of the recurrent neural network. Specifically, the hidden state of the recurrent neural network is partitioned into a plurality of partitions and the plurality of partitions comprises a respective partition for each of a plurality of possible observational features.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting future patient health using neural networks. One of the methods includes receiving electronic health record data for a patient; generating a respective observation embedding for each of the observations, comprising, for each clinical note: processing the sequence of tokens in the clinical note using a clinical note embedding LSTM to generate a respective token embedding for each of the tokens; and generating the observation embedding for the clinical note from the token embeddings; generating an embedded representation, comprising, for each time window: combining the observation embeddings of observations occurring during the time window to generate a patient record embedding; and processing the embedded representation of the electronic health record data using a prediction recurrent neural network to generate a neural network output that characterizes a future health status of the patient.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting future patient health using neural networks. One of the methods includes receiving electronic health record data for a patient; generating a respective observation embedding for each of the observations, comprising, for each clinical note: processing the sequence of tokens in the clinical note using a clinical note embedding LSTM to generate a respective token embedding for each of the tokens; and generating the observation embedding for the clinical note from the token embeddings; generating an embedded representation, comprising, for each time window: combining the observation embeddings of observations occurring during the time window to generate a patient record embedding; and processing the embedded representation of the electronic health record data using a prediction recurrent neural network to generate a neural network output that characterizes a future health status of the patient.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting future patient health using neural networks. One of the methods includes receiving electronic health record data for a patient; generating a respective observation embedding for each of the observations, comprising, for each clinical note: processing the sequence of tokens in the clinical note using a clinical note embedding LSTM to generate a respective token embedding for each of the tokens; and generating the observation embedding for the clinical note from the token embeddings; generating an embedded representation, comprising, for each time window: combining the observation embeddings of observations occurring during the time window to generate a patient record embedding; and processing the embedded representation of the electronic health record data using a prediction recurrent neural network to generate a neural network output that characterizes a future health status of the patient.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training conversational turn analysis neural networks. One of the methods includes obtaining unsupervised training data comprising a plurality of dialogue transcripts; training a turn prediction neural network to perform a turn prediction task on the unsupervised training data using unsupervised learning, wherein: the turn prediction neural network comprises (i) a turn encoder neural network and (ii) a turn decoder neural network; obtaining supervised training data; and training a supervised prediction neural network to perform a supervised prediction task on the supervised training data using supervised learning.