# Predicting Severe Sepsis Using Text from the Electronic Health Record

arXiv: https://arxiv.org/abs/1711.11536 · HTML: https://mlsanity.com/paper/1711.11536/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2017.

## Built on by (most cited first, 2 of 2)

- [Improving Early Sepsis Prediction with Multi Modal Learning](https://mlsanity.com/paper/2107.11094/) (2021)
- [DataWords: Getting Contrarian with Text, Structured Data and Explanations](https://mlsanity.com/paper/2111.05384/) (2021)

## Abstract (arXiv)

Employing a machine learning approach we predict, up to 24 hours prior, a diagnosis of severe sepsis. Strongly predictive models are possible that use only text reports from the Electronic Health Record (EHR), and omit structured numerical data. Unstructured text alone gives slightly better performance than structured data alone, and the combination further improves performance. We also discuss advantages of using unstructured EHR text for modeling, as compared to structured EHR data.