Neural Document Embeddings for Intensive Care Patient Mortality Prediction
This addresses mortality prediction for intensive care patients, representing an incremental improvement in a domain-specific application.
The researchers tackled mortality prediction for ICU patients by analyzing clinical notes, achieving significant performance gains over previous methods, particularly for post-discharge mortality prediction.
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.