# Neural Document Embeddings for Intensive Care Patient Mortality Prediction

arXiv: https://arxiv.org/abs/1612.00467 · HTML: https://mlsanity.com/paper/1612.00467/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2016.

## Built on by (most cited first, 7 of 7)

- [Write It Like You See It: Detectable Differences in Clinical Notes By Race Lead To Differential Model Recommendations](https://mlsanity.com/paper/2205.03931/) (2022)
- [On the Importance of Clinical Notes in Multi-modal Learning for EHR Data](https://mlsanity.com/paper/2212.03044/) (2022)
- [Evaluating the Impact of Social Determinants on Health Prediction in the Intensive Care Unit](https://mlsanity.com/paper/2305.12622/) (2023)
- [An empirical study of using radiology reports and images to improve ICU mortality prediction](https://mlsanity.com/paper/2307.07513/) (2023)
- [Using Machine Learning to Fuse Verbal Autopsy Narratives and Binary Features in the Analysis of Deaths from Hyperglycaemia](https://mlsanity.com/paper/2204.12169/) (2022)
- [Clinical Utility of the Automatic Phenotype Annotation in Unstructured Clinical Notes: ICU Use Cases](https://mlsanity.com/paper/2107.11665/) (2021)
- [Mortality Prediction Models with Clinical Notes Using Sparse Attention at the Word and Sentence Levels](https://mlsanity.com/paper/2212.06267/) (2022)

## Abstract (arXiv)

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.