JMIR Publications

ABSTRACT

Background:

The abundance of online content contributed by patients is a rich source of insight about the lived experience of disease. Patients share disease experiences with other members of the patient and caregiver community, and do so using their own lexicon of words and phrases. This lexicon, and the topics that are communicated using words and phrases belonging to the lexicon, help us better understand disease burden. Insights from social media may ultimately guide clinical development in ways that ensure that future treatments are fit for purpose from the patient’s perspective.

Objective:

We sought insights into the patient experience of COPD by analyzing a substantial corpus of social media content. The corpus was sufficiently large to make manual review and manual coding all but impossible to perform in a consistent and systematic fashion. Advanced analytics are applied to the corpus content in the search for associations between symptoms and impacts across the entire text corpus.

Methods:

We conducted a retrospective, cross-sectional study of 5663 posts sourced from open blogs and online forum posts published by COPD patients between 2016 and August 2019. We apply a novel, neural network approach to identify a lexicon of community words and phrases used by patients to describe their symptoms. We use this lexicon to explore the relationship between COPD symptoms and disease-related impacts.

Results:

We identify a diverse lexicon of community words and phrases for COPD symptoms, including gasping, wheezy, mucus-y and muck. These symptoms are mentioned in association with specific words and phrases for disease impact such as frightening, breathing discomfort and difficulty exercising. Furthermore, we find an association between mucus hypersecretion and moderate disease severity, which distinguishes mucus from the other main COPD symptoms, namely breathlessness and cough.

Conclusions:

We demonstrate the potential of neural networks and advanced analytics to gain patient-focused insights about how each distinct COPD symptom contributes to the burden of chronic and acute respiratory illness. Using a neural network approach, we have identified words and phrases for COPD symptoms that are specific to the patient community. Identifying patterns in the association between symptoms and impacts deepen our understanding of the patient experience of COPD. This approach can be readily applied to other disease areas.