Adapting Phrase-based Machine Translation to Normalise Medical Terms in Social Media Messages

TL;DR

This paper presents a method that adapts phrase-based machine translation and word vectors to normalize social media health-related terms, improving recognition of medical concepts in informal text.

Contribution

It introduces a novel approach combining phrase-based MT and word vectors for medical term normalization in social media messages.

Findings

Outperforms baseline methods by up to 55% in accuracy.

Effective in mapping layman terms to medical concepts.

Applicable to social media data like tweets for health monitoring.

Abstract

Previous studies have shown that health reports in social media, such as DailyStrength and Twitter, have potential for monitoring health conditions (e.g. adverse drug reactions, infectious diseases) in particular communities. However, in order for a machine to understand and make inferences on these health conditions, the ability to recognise when laymen's terms refer to a particular medical concept (i.e.\ text normalisation) is required. To achieve this, we propose to adapt an existing phrase-based machine translation (MT) technique and a vector representation of words to map between a social media phrase and a medical concept. We evaluate our proposed approach using a collection of phrases from tweets related to adverse drug reactions. Our experimental results show that the combination of a phrase-based MT technique and the similarity between word vector representations outperforms the baselines that apply only either of them by up to 55%.