Neural Document Embeddings for Intensive Care Patient Mortality Prediction

Understand

We present an automatic mortality prediction scheme based on the unstructured textual content of clinical notes.

• Proposing a convolutional document embedding approach, our empirical investigation using the MIMIC-III intensive care database shows significant performance gains compared to previously employed methods such as latent topic distributions or generic doc2vec embeddings.
• These improvements are especially pronounced for the difficult problem of post-discharge mortality prediction.