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

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

Publication date: Sep 19, 2023

While scientific knowledge of post-COVID-19 condition (PCC) is growing, there remains significant uncertainty in the definition of the 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. In this study, we aimed to determine the validity and effectiveness of advanced natural language processing approaches built to derive insight into PCC-related patient-reported health outcomes from social media platforms Twitter and Reddit. We extracted PCC-related terms, including symptoms and conditions, and measured their occurrence frequency. We compared the outputs with human annotations and clinical outcomes and tracked symptom and condition term occurrences over time and locations to explore the pipeline’s potential as a surveillance tool. We used bidirectional encoder representations from transformers (BERT) models to extract and normalize PCC symptom and condition terms from English posts on Twitter and Reddit. We compared 2 named entity recognition models and implemented a 2-step normalization task to map extracted terms to unique concepts in standardized terminology. The normalization steps were done using a semantic search approach with BERT biencoders. We evaluated the effectiveness of BERT models in extracting the terms using a human-annotated corpus and a proximity-based score. We also compared the validity and reliability of the extracted and normalized terms to a web-based survey with more than 3000 participants from several countries. UmlsBERT-Clinical had the highest accuracy in predicting entities closest to those extracted by human annotators. Based on our findings, the top 3 most commonly occurring groups of PCC symptom and condition terms were systemic (such as fatigue), neuropsychiatric (such as anxiety and brain fog), and respiratory (such as shortness of breath). In addition, we also found novel symptom and condition terms that had not been categorized in previous studies, such as infection and pain. Regarding the co-occurring symptoms, the pair of fatigue and headaches was among the most co-occurring term pairs across both platforms. Based on the temporal analysis, the neuropsychiatric terms were the most prevalent, followed by the systemic category, on both social media platforms. Our spatial analysis concluded that 42% (10,938/26,247) of the analyzed terms included location information, with the majority coming from the United States, United Kingdom, and Canada. The outcome of our social media-derived pipeline is comparable with the results of peer-reviewed articles relevant to PCC symptoms. Overall, this study provides unique insights into patient-reported health outcomes of PCC and valuable information about the patient’s journey that can help health care providers anticipate future needs. RR2-10. 1101/2022. 12. 14. 22283419.

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Concepts Keywords
Canada entity extraction
Neuropsychiatric entity normalization
Twitter health outcome
long COVID
machine learning
natural language processing
patient-reported outcome
patient-reported symptom
post–COVID-19 condition
social media
transformer models


Type Source Name
disease MESH COVID-19
drug DRUGBANK Factor IX Complex (Human)
disease MESH uncertainty
disease MESH clinical course
disease VO effectiveness
disease VO frequency
disease IDO symptom
disease VO time
disease IDO entity
disease MESH infection
disease VO Canada
drug DRUGBANK Methylphenidate
disease MESH long COVID
disease MESH sequelae
disease IDO acute infection
disease VO organ
disease VO population
disease VO report
drug DRUGBANK Alpha-1-proteinase inhibitor
disease IDO process
drug DRUGBANK Ranitidine
disease IDO country
drug DRUGBANK Methionine
disease VO nose
disease MESH tachycardia
disease MESH cognitive dysfunction
disease MESH neuralgia
disease MESH hair loss
disease MESH weight loss
disease MESH dementia
disease MESH parasomnia
disease MESH pneumonia
disease MESH dysautonomia
disease MESH migraines
disease MESH bradycardia

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