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Using Social Media to Help Understand Patient-Reported Health Outcomes of Post-COVID-19 Condition: Natural Language

Elham Dolatabadi1,2,3, Diana Moyano2, Michael Bales4

  • 1Faculty of Health, School of Health Policy and Management, York University, Toronto, ON, Canada.

Journal of Medical Internet Research
|September 19, 2023
PubMed
Summary
This summary is machine-generated.

Advanced natural language processing of social media data reveals key post-COVID-19 condition (PCC) symptoms like fatigue and brain fog. This approach offers valuable patient-reported health outcomes for understanding PCC

Keywords:
PCCPRORedditTwitterbidirectional encoder representations from transformersentity extractionentity normalizationhealth outcomelong COVIDmachine learningnatural language processingpatient-reported outcomepatient-reported symptompost–COVID-19 conditionsocial mediasymptomtransformer models

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Public Health Surveillance

Background:

  • Growing scientific knowledge of post-COVID-19 condition (PCC) but significant uncertainty remains regarding its definition, clinical course, and impact on daily functioning.
  • Social media platforms offer high-resolution, patient- and caregiver-generated insights into health outcomes, potentially capturing experiences missed by clinicians.

Purpose of the Study:

  • To determine the validity and effectiveness of advanced natural language processing (NLP) approaches for deriving patient-reported health outcomes in PCC from social media.
  • To extract, measure frequency, and track PCC-related symptoms and conditions over time and location using NLP.

Main Methods:

  • Utilized bidirectional encoder representations from transformers (BERT) models for extracting and normalizing PCC symptom/condition terms from Twitter and Reddit.
  • Compared two named entity recognition models and employed a two-step normalization using semantic search with BERT biencoders.
  • Evaluated model effectiveness via human annotation and proximity scoring, and compared results with a large-scale web-based survey.

Main Results:

  • UmlsBERT-Clinical demonstrated high accuracy in entity prediction, comparable to human annotators.
  • Top PCC symptom groups identified: systemic (e.g., fatigue), neuropsychiatric (e.g., anxiety, brain fog), and respiratory (e.g., shortness of breath).
  • Novel terms like 'infection' and 'pain' were identified; 'fatigue' and 'headaches' were common co-occurring symptoms. Neuropsychiatric terms were most prevalent temporally.

Conclusions:

  • The social media-derived NLP pipeline yields results comparable to peer-reviewed literature on PCC symptoms.
  • This study provides unique insights into patient-reported PCC health outcomes and the patient journey.
  • Findings can assist healthcare providers in anticipating future patient needs related to post-COVID-19 condition.