Automating the extraction of otology symptoms from clinic letters: a methodological study using natural language processing.

ABSTRACT:

Background

Most healthcare data is in an unstructured format that requires processing to make it usable for research. Generally, this is done manually, which is both time-consuming and poorly scalable. Natural language processing (NLP) using machine learning offers a method to automate data extraction. In this paper we describe the development of a set of NLP models to extract and contextualise otology symptoms from free text documents.

Methods

A dataset of 1,148 otology clinic letters written between 2009 - 2011, from a London NHS hospital, were manually annotated and used to train a hybrid dictionary and machine learning NLP model to identify six key otological symptoms: hearing loss, impairment of balance, otalgia, otorrhoea, tinnitus and vertigo. Subsequently, a set of B

SUBMITTER: Joshi N 

PROVIDER: S-EPMC12482202 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature