# Learning Patient Representations from Text

arXiv: https://arxiv.org/abs/1805.02096 · HTML: https://mlsanity.com/paper/1805.02096/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2018.

## Built on by (most cited first, 4 of 4)

- [Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review](https://mlsanity.com/paper/2107.02975/) (2021)
- [Enriching Unsupervised User Embedding via Medical Concepts](https://mlsanity.com/paper/2203.10627/) (2022)
- [Assessing mortality prediction through different representation models based on concepts extracted from clinical notes](https://mlsanity.com/paper/2207.10872/) (2022)
- [A Study into patient similarity through representation learning from medical records](https://mlsanity.com/paper/2104.14229/) (2021)

## Abstract (arXiv)

Mining electronic health records for patients who satisfy a set of predefined criteria is known in medical informatics as phenotyping. Phenotyping has numerous applications such as outcome prediction, clinical trial recruitment, and retrospective studies. Supervised machine learning for phenotyping typically relies on sparse patient representations such as bag-of-words. We consider an alternative that involves learning patient representations. We develop a neural network model for learning patient representations and show that the learned representations are general enough to obtain state-of-the-art performance on a standard comorbidity detection task.