Towards Providing Clinical Insights on Long Covid from Twitter Data

Bhambhoria, Rohan; Saab, Jad; Uppal, Sara; Li, Xin; Yakimovich, Artur; Bhatti, Junaid Ahmad; Valdamudi, Nirma Khatri; Moyano, Diana; Bales, Michael E.; Dolatabadi, Elham; Koçak, Sedef Akinli

doi:10.1007/978-3-031-14771-5_19

Cited by 3 publications

(8 citation statements)

References 24 publications

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“…To further pseudonymize the data, we transformed special characters in the tweets or Reddit posts to lowercase and extracted contractions. The supplementary materials and our previous work describe more details about the data collection and preprocessing steps [28].…”

Section: Methodology Data Collectionmentioning

confidence: 99%

“…Both models' performances are compared against human annotators in terms of the proximity score. As shown in the table, we also included entity extraction results by the data augmentation approach UMLS MetaMap (+AMIA) introduced in our earlier work [28]. UMLS MetaMap (+AMIA) uses the MetaMapLite tool to extract entities associating with UMLS' Concept Unique Identifiers (CUIs) and augments the results with a manually annotated dataset consisting of clinical concepts and colloquial expressions (e.g., brain fog) from tweets.…”

Section: Evaluation Of Extraction Of Syco Termsmentioning

confidence: 99%

“…In response to the emergence of long COVID, we built an NLP pipeline to facilitate extracting information from user-reported experiences in social media platforms [28]. In this study, we examined the validity and effectiveness of our NLP pipeline to provide insights into patient-reported long COVID-related health outcomes across two popular social media platforms; Twitter and Reddit.…”

Section: Introductionmentioning

confidence: 99%

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Using Social Media to Help Understand Long COVID Patient Reported Health Outcomes: A Natural Language Processing Approach

Dolatabadi

Moyano

Bales

et al. 2022

Preprint

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Background: There remains 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. Methodology: 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. Result: 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. Conclusion: 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.

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Section: Methodology Data Collectionmentioning

confidence: 99%

Section: Evaluation Of Extraction Of Syco Termsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using Social Media to Help Understand Long COVID Patient Reported Health Outcomes: A Natural Language Processing Approach

Dolatabadi

Moyano

Bales

et al. 2022

Preprint

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“…Besides the application of patient engagement to clinical AI, AI technology can also enhance patient engagement in many ways. We have seen industry use state-of-the-art entityrecognition models and extraction methods to provide clinical insights prior to defining subsequent downstream tasks from social media data relating to Post Covid-19 Condition colloquially known as ''LongCOVID'' [21].…”

Section: How Can Stakeholders Work Better With Patients?mentioning

confidence: 99%

Commentary: Patient Perspectives on Artificial Intelligence; What have We Learned and How Should We Move Forward?

Camaradou

Hogg

2023

Adv Ther

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Artificial intelligence (AI) in healthcare has now begun to make its contributions to real-world patient care with varying degrees of both public and clinical acceptability around it. The heavy investment from governments, industry and academia needed to reach this point has helped to surface different perspectives on AI. As clinical AI applications become a reality, however, there is an increasing need to harness and integrate patient perspectives, which address the distinct needs of different populations, healthcare systems and clinical problems more closely. Despite this need, patient perspectives on AI implementation have little presence in academic literature and within implementation science and are not sufficiently considered throughout the MedTech and eHealthtech product development cycle, which brings its own challenges and opportunities. This joint patient expert/clinician commentary aims to briefly summarise views on AI. It reflects upon recommendations on how stakeholders such as clinicians and Health & MedTech small and medium-sized enterprises (SMEs) can make practical usage of these views. The recommendations of the authors centre around how to work better with patients to enable both product centric and patient centric innovation and person-centred care.

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“…In response to the emergence of PCC, we developed an NLP pipeline as shown in Figure 1 to facilitate extracting information from user-reported experiences in social media platforms [ 32 ]. In this study, we examined the validity and effectiveness of our NLP pipeline to provide insights into patient-reported PCC-related health outcomes across 2 popular social media platforms, Twitter and Reddit.…”

Section: Introductionmentioning

confidence: 99%

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

Dolatabadi¹,

Moyano²,

Bales³

et al. 2023

J Med Internet Res

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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

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Towards Providing Clinical Insights on Long Covid from Twitter Data

Cited by 3 publications

References 24 publications

Using Social Media to Help Understand Long COVID Patient Reported Health Outcomes: A Natural Language Processing Approach

Using Social Media to Help Understand Long COVID Patient Reported Health Outcomes: A Natural Language Processing Approach

Commentary: Patient Perspectives on Artificial Intelligence; What have We Learned and How Should We Move Forward?

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

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