A Comparative Study on Patient Language across Therapeutic Domains for Effective Patient Voice Classification in Online Health Discussions

TL;DR

This study analyzes linguistic features of patient language across therapeutic domains on social media to improve patient voice classification, demonstrating the importance of text similarity analysis and fine-tuning language models for accuracy.

Contribution

It introduces the first comprehensive analysis of patient language patterns across domains and applies fine-tuning of language models for effective patient voice classification.

Findings

Linguistic and statistical text similarity are crucial for identifying patient voices.

Significant differences exist in patient expression across diseases and therapeutic domains.

Fine-tuned language models achieve high accuracy in classifying patient voices.

Abstract

There exists an invisible barrier between healthcare professionals' perception of a patient's clinical experience and the reality. This barrier may be induced by the environment that hinders patients from sharing their experiences openly with healthcare professionals. As patients are observed to discuss and exchange knowledge more candidly on social media, valuable insights can be leveraged from these platforms. However, the abundance of non-patient posts on social media necessitates filtering out such irrelevant content to distinguish the genuine voices of patients, a task we refer to as patient voice classification. In this study, we analyse the importance of linguistic characteristics in accurately classifying patient voices. Our findings underscore the essential role of linguistic and statistical text similarity analysis in identifying common patterns among patient groups. These results allude to even starker differences in the way patients express themselves at a disease level and across various therapeutic domains. Additionally, we fine-tuned a pre-trained Language Model on the combined datasets with similar linguistic patterns, resulting in a highly accurate automatic patient voice classification. Being the pioneering study on the topic, our focus on extracting authentic patient experiences from social media stands as a crucial step towards advancing healthcare standards and fostering a patient-centric approach.