JMIR Publications

ABSTRACT

Background:

There remains to be significant uncertainty in the definition of the long COVID disease, its expected clinical course, and its impact on daily functioning. Social media platforms can generate valuable insights into patient-reported health outcomes as the content is produced at high resolution by patients and caregivers, representing experiences that may be unavailable to most clinicians.

Objective:

We aim to determine the validity and effectiveness of advanced NLP approaches built to derive insight into Long COVID-related patient-reported health outcomes from social media platforms.

Methods:

We use Transformer-based BERT models to extract and normalize long COVID Symptoms and Conditions (SyCo) from English posts on Twitter and Reddit. Furthermore, we estimate the occurrence and co-occurrence of SyCo terms at any point or across time and locations. Finally, we compare the extracted health outcomes with human annotations and highly utilized clinical outcomes grounded in the medical literature.

Results:

Based on our findings, the top three most commonly occurring groups of long COVID symptoms are systemic (such as “fatigue”), neuropsychiatric (such as “anxiety“ and “brain fog”), and respiratory (such as “shortness of breath”). Regarding the co-occurring symptoms, the pair of ‘fatigue & headaches’ is most common. In addition, we show that other conditions, such as infection, hair loss, and weight loss, as well as mentions of other diseases, such as flu, cancer, or Lyme disease, are among the top reported terms by social media users.

Conclusions:

The outcome of our social media-derived pipeline is comparable with the outcomes of peer-reviewed articles relevant to long COVID symptoms. Overall, this study provides unique insights into patient-reported health outcomes from long COVID and valuable information about the patient’s journey that can help healthcare providers anticipate future needs. Clinical Trial: N/A