# Seeing The Whole Patient: Using Multi-Label Medical Text Classification Techniques to Enhance Predictions of Medical Codes

arXiv: https://arxiv.org/abs/2004.00430 · HTML: https://mlsanity.com/paper/2004.00430/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2020.

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

- [Predicting COVID-19 Patient Shielding: A Comprehensive Study](https://mlsanity.com/paper/2110.00183/) (2021)
- [Improving Predictions of Tail-end Labels using Concatenated BioMed-Transformers for Long Medical Documents](https://mlsanity.com/paper/2112.01718/) (2021)

## Abstract (arXiv)

Machine learning-based multi-label medical text classifications can be used to enhance the understanding of the human body and aid the need for patient care. We present a broad study on clinical natural language processing techniques to maximise a feature representing text when predicting medical codes on patients with multi-morbidity. We present results of multi-label medical text classification problems with 18, 50 and 155 labels. We compare several variations to embeddings, text tagging, and pre-processing. For imbalanced data we show that labels which occur infrequently, benefit the most from additional features incorporated in embeddings. We also show that high dimensional embeddings pre-trained using health-related data present a significant improvement in a multi-label setting, similarly to the way they improve performance for binary classification. High dimensional embeddings from this research are made available for public use.