Clinical notes are an efficient way to record patient information but are notoriously hard to decipher for non-experts. Automatically simplifying medical text can empower patients with valuable information about their health, while saving clinicians time. We present a novel approach to automated simplification of medical text based on word frequencies and language modelling, grounded on medical ontologies enriched with layman terms. We release a new dataset of pairs of publicly available medical sentences and a version of them simplified by clinicians. Also, we define a novel text simplification metric and evaluation framework, which we use to conduct a large-scale human evaluation of our method against the state of the art. Our method based on a language model trained on medical forum data generates simpler sentences while preserving both grammar and the original meaning, surpassing the current state of the art.
@article{arxiv.2112.12672,
title = {Towards more patient friendly clinical notes through language models and ontologies},
author = {Francesco Moramarco and Damir Juric and Aleksandar Savkov and Jack Flann and Maria Lehl and Kristian Boda and Tessa Grafen and Vitalii Zhelezniak and Sunir Gohil and Alex Papadopoulos Korfiatis and Nils Hammerla},
journal= {arXiv preprint arXiv:2112.12672},
year = {2022}
}