Towards more patient friendly clinical notes through language models and ontologies.

Autor:	Moramarco F; Babylon Health, London, UK., Juric D; Babylon Health, London, UK., Savkov A; Babylon Health, London, UK., Flann J; Babylon Health, London, UK., Lehl M; Babylon Health, London, UK., Boda K; Babylon Health, London, UK., Grafen T; Babylon Health, London, UK., Zhelezniak V; Babylon Health, London, UK., Gohil S; Babylon Health, London, UK., Korfiatis AP; Babylon Health, London, UK., Hammerla N; Babylon Health, London, UK.
Jazyk:	angličtina
Zdroj:	AMIA ... Annual Symposium proceedings. AMIA Symposium [AMIA Annu Symp Proc] 2022 Feb 21; Vol. 2021, pp. 881-890. Date of Electronic Publication: 2022 Feb 21 (Print Publication: 2021).
Abstrakt:	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. (©2021 AMIA - All rights reserved.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu