2021/12/23 by Francesco Moramarco, Damir Jurić, Moramarco, Francesco +19 · 1 citation
Computer Science · Health Professions · #Text Readability and Simplification #Topic Modeling #Health Literacy and Information Accessibility
paper · pdf · doi:10.48550/arxiv.2112.12672
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.