# Prediction of ICD Codes with Clinical BERT Embeddings and Text Augmentation with Label Balancing using MIMIC-III

arXiv: https://arxiv.org/abs/2008.10492 · HTML: https://mlsanity.com/paper/2008.10492/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2020.

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

- [BERT for Long Documents: A Case Study of Automated ICD Coding](https://mlsanity.com/paper/2211.02519/) (2022)
- [Examining Imbalance Effects on Performance and Demographic Fairness of Clinical Language Models](https://mlsanity.com/paper/2412.17803/) (2024)
- [Calibrated Optimal Decision Making with Multiple Data Sources and Limited Outcome](https://mlsanity.com/paper/2104.10554/) (2021)
- [Meta Learning for Few-Shot Medical Text Classification](https://mlsanity.com/paper/2212.01552/) (2022)

## Abstract (arXiv)

This paper achieves state of the art results for the ICD code prediction task using the MIMIC-III dataset. This was achieved through the use of Clinical BERT (Alsentzer et al., 2019). embeddings and text augmentation and label balancing to improve F1 scores for both ICD Chapter as well as ICD disease codes. We attribute the improved performance mainly to the use of novel text augmentation to shuffle the order of sentences during training. In comparison to the Top-32 ICD code prediction (Keyang Xu, et. al.) with an F1 score of 0.76, we achieve a final F1 score of 0.75 but on a total of the top 50 ICD codes.