# Improved Hierarchical Patient Classification with Language Model Pretraining over Clinical Notes

arXiv: https://arxiv.org/abs/1909.03039 · HTML: https://mlsanity.com/paper/1909.03039/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2019.

## Built on by (most cited first, 5 of 5)

- [MUFASA: Multimodal Fusion Architecture Search for Electronic Health Records](https://mlsanity.com/paper/2102.02340/) (2021)
- [Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification](https://mlsanity.com/paper/2106.10826/) (2021)
- [Explainable Health Risk Predictor with Transformer-based Medicare Claim Encoder](https://mlsanity.com/paper/2105.09428/) (2021)
- [Generalized and Transferable Patient Language Representation for Phenotyping with Limited Data](https://mlsanity.com/paper/2103.00482/) (2021)
- [GRU-TV: Time- and velocity-aware GRU for patient representation on multivariate clinical time-series data](https://mlsanity.com/paper/2205.04892/) (2022)

## Abstract (arXiv)

Clinical notes in electronic health records contain highly heterogeneous writing styles, including non-standard terminology or abbreviations. Using these notes in predictive modeling has traditionally required preprocessing (e.g. taking frequent terms or topic modeling) that removes much of the richness of the source data. We propose a pretrained hierarchical recurrent neural network model that parses minimally processed clinical notes in an intuitive fashion, and show that it improves performance for discharge diagnosis classification tasks on the Medical Information Mart for Intensive Care III (MIMIC-III) dataset, compared to models that treat the notes as an unordered collection of terms or that conduct no pretraining. We also apply an attribution technique to examples to identify the words that the model uses to make its prediction, and show the importance of the words' nearby context.