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

BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1101/2022.12.14.22283419.

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

[This corrects the article DOI: 10.2196/45767.].

Shape analysis of the carpal joint in healthy and septic arthritis in newborn calves.

Septic arthritis is common in newborn calves due to poor birth and housing hygiene. This study investigated the pathological deformities caused by arthritis in the carpal bones of calves using geometric morphometry. The changes in the carpal joint bones of newborn calves with septic arthritis were examined through shape analysis. The study included 20 healthy Simmental calves and 30 Simmental calves with septic arthritis. Dorso-palmar x-ray images of the carpal joint were taken, and geometric morphometry was performed on these images using 25 landmarks. The first principal components (PC1) represented 26.92% of the total variation, while PC2 represented 13.84%. One of the most significant shape changes with increasing PC1 occurred in the os carpi intermedium. The study found that it was statistically possible to discriminate between radiometric carpal joint images of Simmental calves in the control and arthritis groups using geometric morphometry. In newborn calves with septic arthritis, the trochlea radi was located more proximally. There was an enlargement of the os carpi intermedium and a tendency towards the os carpi ulnare in female calves with septic arthritis. These results indicate significant bone deformation due to septic arthritis. Geometric morphometric methods can be clinically useful, as demonstrated in this study. Researchers can statistically explore these shape analyses, opening new avenues for research in this field. This method not only enhances our understanding of morphological changes but also provides a framework for clinical investigations and discoveries in related areas.