Processing clinical notes using recurrent neural networks

One or more non-transitory computer-readable storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations comprising:

• receiving electronic health record data for a patient,
• the electronic health record data comprising one or more observations of each of a plurality of different feature types, and
• the plurality of different feature types including a clinical note feature type, wherein each observation of the clinical note feature type is a clinical note comprising a respective sequence of text tokens;
• generating a respective observation embedding for each of the observations, comprising, for each clinical note:
• processing the sequence of text tokens in the clinical note using a clinical note embedding long short-term memory (LSTM) neural network to generate a respective token embedding for each of the text tokens;
• and
• generating the observation embedding for the clinical note from the token embeddings for the text tokens in the clinical note, comprising computing a weighted sum of the token embeddings, with each token embedding being weighted by a corresponding weight;
• generating an embedded representation of the electronic health record data, wherein the embedded representation comprises a respective patient record embedding corresponding to each of a plurality of time windows, and wherein generating the embedded representation comprises, for each time window:
• combining the observation embeddings of observations occurring during the time window to generate the patient record embedding corresponding to the time window;
• 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 after the last time window in the embedded representation.

A computer-implemented method comprising:

• receiving electronic health record data for a patient,
• the electronic health record data comprising one or more observations of each of a plurality of different feature types, and
• the plurality of different feature types including a clinical note feature type, wherein each observation of the clinical note feature type is a clinical note comprising a respective sequence of text tokens;
• generating a respective observation embedding for each of the observations, comprising, for each clinical note:
• processing the sequence of text tokens in the clinical note using a clinical note embedding long short-term memory (LSTM) neural network to generate a respective token embedding for each of the text tokens;
• and
• generating the observation embedding for the clinical note from the token embeddings for the text tokens in the clinical note, comprising computing a weighted sum of the token embeddings, with each token embedding being weighted by a corresponding weight;
• generating an embedded representation of the electronic health record data, wherein the embedded representation comprises a respective patient record embedding corresponding to each of a plurality of time windows, and wherein generating the embedded representation comprises, for each time window:
• combining the observation embeddings of observations occurring during the time window to generate the patient record embedding corresponding to the time window;
• 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 after the last time window in the embedded representation.