COVID-19 Vaccine Hesitancy: A Global Public Health and Risk Modelling Framework Using an Environmental Deep Neural Network, Sentiment Classification with Text Mining and Emotional Reactions from COVID-19 Vaccination Tweets.

Popular social media platforms, such as Twitter, have become an excellent source of information with their swift information dissemination. Individuals with different backgrounds convey their opinions through social media platforms. Consequently, these platforms have become a profound instrument for collecting enormous datasets. We believe that compiling, organizing, exploring, and analyzing data from social media platforms, such as Twitter, can offer various perspectives to public health organizations and decision makers in identifying factors that contribute to vaccine hesitancy. In this study, public tweets were downloaded daily from Tweeter using the Tweeter API. Before performing computation, the tweets were preprocessed and labeled. Vocabulary normalization was based on stemming and lemmatization. The NRCLexicon technique was deployed to convert the tweets into ten classes: positive sentiment, negative sentiment, and eight basic emotions (joy, trust, fear, surprise, anticipation, anger, disgust, and sadness). t-test was used to check the statistical significance of the relationships among the basic emotions. Our analysis shows that the p-values of joy-sadness, trust-disgust, fear-anger, surprise-anticipation, and negative-positive relations are close to zero. Finally, neural network architectures, including 1DCNN, LSTM, Multiple-Layer Perceptron, and BERT, were trained and tested in a COVID-19 multi-classification of sentiments and emotions (positive, negative, joy, sadness, trust, disgust, fear, anger, surprise, and anticipation). Our experiment attained an accuracy of 88.6% for 1DCNN at 1744 s, 89.93% accuracy for LSTM at 27,597 s, while MLP achieved an accuracy of 84.78% at 203 s. The study results show that the BERT model performed the best, with an accuracy of 96.71% at 8429 s.

Use of data mining approaches to explore the association between type 2 diabetes mellitus with SARS-CoV-2.

BACKGROUND AND OBJECTIVE: Corona virus causes respiratory tract infections in mammals. The latest type of Severe Acute Respiratory Syndrome Corona-viruses 2 (SARS-CoV-2), Corona virus spread in humans in December 2019 in Wuhan, China. The purpose of this study was to investigate the relationship between type 2 diabetes mellitus (T2DM), and their biochemical and hematological factors with the level of infection with COVID-19 to improve the treatment and management of the disease. MATERIAL AND METHOD: This study was conducted on a population of 13,170 including 5780 subjects with SARS-COV-2 and 7390 subjects without SARS-COV-2, in the age range of 35-65 years. Also, the associations between biochemical factors, hematological factors, physical activity level (PAL), age, sex, and smoking status were investigated with the COVID-19 infection. RESULT: Data mining techniques such as logistic regression (LR) and decision tree (DT) algorithms were used to analyze the data. The results using the LR model showed that in biochemical factors (Model I) creatine phosphokinase (CPK) (OR: 1.006 CI 95% (1.006,1.007)), blood urea nitrogen (BUN) (OR: 1.039 CI 95% (1.033, 1.047)) and in hematological factors (Model II) mean platelet volume (MVP) (OR: 1.546 CI 95% (1.470, 1.628)) were significant factors associated with COVID-19 infection. Using the DT model, CPK, BUN, and MPV were the most important variables. Also, after adjustment for confounding factors, subjects with T2DM had higher risk for COVID-19 infection. CONCLUSION: There was a significant association between CPK, BUN, MPV and T2DM with COVID-19 infection and T2DM appears to be important in the development of COVID-19 infection.

Extracting Temporal Relationships in EHR: Application to COVID-19 Patients.

Association rules are one of the most used data mining techniques. The first proposals have considered relations over time in different ways, resulting in the so-called Temporal Association Rules (TAR). Although there are some proposals to extract association rules in OLAP systems, to the best of our knowledge, there is no method proposed to extract temporal association rules over multidimensional models in these kinds of systems. In this paper we study the adaptation of TAR to multidimensional structures, identifying the dimension that establishes the number of transactions and how to find time relative correlations between the other dimensions. A new method called COGtARE is presented as an extension of a previous approach proposed to reduce the complexity of the resulting set of association rules. The method is tested in application to COVID-19 patients data.

A theoretical model of factors influencing online consumer purchasing behavior through electronic word of mouth data mining and analysis.

The coronavirus disease 2019 pandemic has impacted and changed consumer behavior because of a prolonged quarantine and lockdown. This study proposed a theoretical framework to explore and define the influencing factors of online consumer purchasing behavior (OCPB) based on electronic word-of-mouth (e-WOM) data mining and analysis. Data pertaining to e-WOM were crawled from smartphone product reviews from the two most popular online shopping platforms in China, Jingdong.com and Taobao.com. Data processing aimed to filter noise and translate unstructured data from complex text reviews into structured data. The machine learning based K-means clustering method was utilized to cluster the influencing factors of OCPB. Comparing the clustering results and Kotler's five products level, the influencing factors of OCPB were clustered around four categories: perceived emergency context, product, innovation, and function attributes. This study contributes to OCPB research by data mining and analysis that can adequately identify the influencing factors based on e-WOM. The definition and explanation of these categories may have important implications for both OCPB and e-commerce.

A novel corpus of molecular to higher-order events that facilitates the understanding of the pathogenic mechanisms of idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a severe and progressive chronic fibrosing interstitial lung disease with causes that have remained unclear to date. Development of effective treatments will require elucidation of the detailed pathogenetic mechanisms of IPF at both the molecular and cellular levels. With a biomedical corpus that includes IPF-related entities and events, text-mining systems can efficiently extract such mechanism-related information from huge amounts of literature on the disease. A novel corpus consisting of 150 abstracts with 9297 entities intended for training a text-mining system was constructed to clarify IPF-related pathogenetic mechanisms. For this corpus, entity information was annotated, as were relation and event information. To construct IPF-related networks, we also conducted entity normalization with IDs assigned to entities. Thereby, we extracted the same entities, which are expressed differently. Moreover, IPF-related events have been defined in this corpus, in contrast to existing corpora. This corpus will be useful to extract IPF-related information from scientific texts. Because many entities and events are related to lung diseases, this freely available corpus can also be used to extract information related to other lung diseases such as lung cancer and interstitial pneumonia caused by COVID-19.

User review analysis of dating apps based on text mining.

With the continuous development of information technology, more and more people have become to use online dating apps, and the trend has been exacerbated by the COVID-19 pandemic in these years. However, there is a phenomenon that most of user reviews of mainstream dating apps are negative. To study this phenomenon, we have used topic model to mine negative reviews of mainstream dating apps, and constructed a two-stage machine learning model using data dimensionality reduction and text classification to classify user reviews of dating apps. The research results show that: firstly, the reasons for the current negative reviews of dating apps are mainly concentrated in the charging mechanism, fake accounts, subscription and advertising push mechanism and matching mechanism in the apps, proposed corresponding improvement suggestions are proposed by us; secondly, using principal component analysis to reduce the dimensionality of the text vector, and then using XGBoost model to learn the low-dimensional data after oversampling, a better classification accuracy of user reviews can be obtained. We hope These findings can help dating apps operators to improve services and achieve sustainable business operations of their apps.

A novel algorithm of data mining to predict future scenarios of COVID-19 pandemic.

COVID-19, a novel coronavirus, is an ongoing global pandemic that has outbroken recently and spread to almost every part of the world. Several factors of this pandemic are still unknown to the world, which causes uncertainty to prepare a strategic plan to cope with this disease effectively and securing the future. A large number of research is in progress or expected to start shortly on the basis of the publicly available datasets of this deadly pandemic. The data are available in multiple formats that include geospatial data, medical data, demographic data, and time-series data. In this study, we propose a data mining method to classify and forecast the time-series pandemic data in an attempt to predict the expected end of this pandemic in a particular region. Based on the COVID-19 data obtained from several countries around the world, a naïve Bayes classifier is built, which may classify the affected countries into one of the following four categories: critical, unsustainable, sustainable, and closed. The pandemic data collected from online sources are preprocessed, labeled, and classified by using different data mining techniques. A new clustering technique is also proposed to predict the expected end of the pandemic in different countries. A method to preprocess the data before applying the clustering technique is also proposed. The results of naïve Bayes classification and clustering techniques are validated based on accuracy, execution time, and other statistical measures.

The New Version of the ANDDigest Tool with Improved AI-Based Short Names Recognition.

The body of scientific literature continues to grow annually. Over 1.5 million abstracts of biomedical publications were added to the PubMed database in 2021. Therefore, developing cognitive systems that provide a specialized search for information in scientific publications based on subject area ontology and modern artificial intelligence methods is urgently needed. We previously developed a web-based information retrieval system, ANDDigest, designed to search and analyze information in the PubMed database using a customized domain ontology. This paper presents an improved ANDDigest version that uses fine-tuned PubMedBERT classifiers to enhance the quality of short name recognition for molecular-genetics entities in PubMed abstracts on eight biological object types: cell components, diseases, side effects, genes, proteins, pathways, drugs, and metabolites. This approach increased average short name recognition accuracy by 13%.

A text-mining study on emotional cognition, understanding, and preventative behaviors during the COVID-19 pandemic.

BACKGROUND: This study aimed to look at emotions perceived about the attributes, prevention, diagnosis, and treatment of infectious diseases related to coronavirus disease (COVID-19) that were widespread across the world and identify their relevance to knowledge about infectious diseases and preventative behaviors. METHODS: Texts to measure emotional cognition were selected through a pre-test, and 282 people were chosen as participants based on the survey conducted for 20 days from August 19 to August 29, 2020, created with Google Forms. IBM SPSS Statistics 25.0 was used for the primary analysis, and the SNA package in R (version 4.0.2) was utilized to conduct the network analysis. RESULTS: It was found that universal negative emotions such as feeling "anxious" (65.5%), "afraid" (46.1%), and "scared" (32.7%) commonly appeared among most people. Also, they were found to be feeling both positive ("caring" [42.3%] and "strict" [28.2%]) and negative ("frustrating" [39.1%] and "isolated" [31.0%]) emotions about efforts to prevent and curb the spread of COVID-19. In terms of emotional cognition for the diagnosis and treatment of such diseases, "reliable" (43.3%) took the biggest ratio among the replies. The level of understanding about infectious diseases showed differences in emotional cognition, thereby affecting people's emotions. However, no differences were found in the practice of preventative behaviors. CONCLUSIONS: Emotions associated with cognition in the context of pandemic infectious diseases have been found to be mixed. Furthermore, it can be seen that feelings vary depending on the degree of understanding of the infectious disease.

Chemical identification and indexing in full-text articles: an overview of the NLM-Chem track at BioCreative VII.

The BioCreative National Library of Medicine (NLM)-Chem track calls for a community effort to fine-tune automated recognition of chemical names in the biomedical literature. Chemicals are one of the most searched biomedical entities in PubMed, and-as highlighted during the coronavirus disease 2019 pandemic-their identification may significantly advance research in multiple biomedical subfields. While previous community challenges focused on identifying chemical names mentioned in titles and abstracts, the full text contains valuable additional detail. We, therefore, organized the BioCreative NLM-Chem track as a community effort to address automated chemical entity recognition in full-text articles. The track consisted of two tasks: (i) chemical identification and (ii) chemical indexing. The chemical identification task required predicting all chemicals mentioned in recently published full-text articles, both span [i.e. named entity recognition (NER)] and normalization (i.e. entity linking), using Medical Subject Headings (MeSH). The chemical indexing task required identifying which chemicals reflect topics for each article and should therefore appear in the listing of MeSH terms for the document in the MEDLINE article indexing. This manuscript summarizes the BioCreative NLM-Chem track and post-challenge experiments. We received a total of 85 submissions from 17 teams worldwide. The highest performance achieved for the chemical identification task was 0.8672 F-score (0.8759 precision and 0.8587 recall) for strict NER performance and 0.8136 F-score (0.8621 precision and 0.7702 recall) for strict normalization performance. The highest performance achieved for the chemical indexing task was 0.6073 F-score (0.7417 precision and 0.5141 recall). This community challenge demonstrated that (i) the current substantial achievements in deep learning technologies can be utilized to improve automated prediction accuracy further and (ii) the chemical indexing task is substantially more challenging. We look forward to further developing biomedical text-mining methods to respond to the rapid growth of biomedical literature. The NLM-Chem track dataset and other challenge materials are publicly available at https://ftp.ncbi.nlm.nih.gov/pub/lu/BC7-NLM-Chem-track/. Database URL https://ftp.ncbi.nlm.nih.gov/pub/lu/BC7-NLM-Chem-track/.

Knowledge Adaptive Multi-Way Matching Network for Biomedical Named Entity Recognition via Machine Reading Comprehension.

Rapid and effective utilization of biomedical literature is paramount to combat diseases like COVID19. Biomedical named entity recognition (BioNER) is a fundamental task in text mining that can help physicians accelerate knowledge discovery to curb the spread of the COVID-19 epidemic. Recent approaches have shown that casting entity extraction as the machine reading comprehension task can significantly improve model performance. However, two major drawbacks impede higher success in identifying entities (1) ignoring the use of domain knowledge to capture the context beyond sentences and (2) lacking the ability to deeper understand the intent of questions. In this paper, to remedy this, we introduce and explore external domain knowledge which cannot be implicitly learned in text sequence. Previous works have focused more on text sequence and explored little of the domain knowledge. To better incorporate domain knowledge, a multi-way matching reader mechanism is devised to model representations of interaction between sequence, question and knowledge retrieved from Unified Medical Language System (UMLS). Benefiting from these, our model can better understand the intent of questions in complex contexts. Experimental results indicate that incorporating domain knowledge can help to obtain competitive results across 10 BioNER datasets, achieving absolute improvement of up to 2.02% in the f1 score.

COVID-19 related discrimination in Japan: A preliminary analysis utilizing text-mining.

ABSTRACT: To assess the general Japanese population's thoughts on coronavirus disease of 2019 related discrimination by Tweets.Tweets were retrieved from search queries using the keywords "health care providers and discrimination (no hashtags)" and "corona and rural area (no hashtags)" via the Twitter application programming interface. Subsequently, a text-mining analysis was conducted on tokenized text data. R version 4.0.2 was used for the analysis.In total, 51,906 tweets for "corona and health care providers", 59,560 tweets for "corona and rural" were obtained between the search period of July 29, 2020 and September 30, 2020. The most common 20 words from the tokenized text data were translated to English. Word clouds with the original Japanese words are presented.Tweets for corona and health care providers did not suggest significant evidence of discrimination toward health care providers on Twitter. Results for corona and rural area, however, showed the unexpected word "murahachibu" (an outmoded word meaning ostracism), suggesting persistent strong social pressure to prevent bringing the disease to the community. This kind of pressure may not be supported by scientific facts. These results demonstrate the need for continued educational efforts to disseminate factual information to the public.

Using Text Mining and Data Visualization Approaches for Investigating Mental Illness from the Perspective of Traditional Chinese Medicine.

Background and Objectives. Anxiety and depressive disorders are the most prevalent mental disorders, and due to the COVID-19 pandemic, more people are suffering from anxiety and depressive disorders, and a considerable fraction of COVID-19 survivors have a variety of persistent neuropsychiatric problems after the initial infection. Traditional Chinese Medicine (TCM) offers a different perspective on mental disorders from Western biomedicine. Effective management of mental disorders has become an increasing concern in recent decades due to the high social and economic costs involved. This study attempts to express and ontologize the relationships between different mental disorders and physical organs from the perspective of TCM, so as to bridge the gap between the unique terminology used in TCM and a medical professional. Materials and Methods. Natural language processing (NLP) is introduced to quantify the importance of different mental disorder descriptions relative to the five depots and two palaces, stomach and gallbladder, through the classical medical text Huangdi Neijing and construct a mental disorder ontology based on the TCM classic text. Results. The results demonstrate that our proposed framework integrates NLP and data visualization, enabling clinicians to gain insights into mental health, in addition to biomedicine. According to the results of the relationship analysis of mental disorders, depots, palaces, and symptoms, the organ/depot most related to mental disorders is the heart, and the two most important emotion factors associated with mental disorders are anger and worry & think. The mental disorders described in TCM are related to more than one organ (depot/palace). Conclusion. This study complements recent research delving into co-relations or interactions between mental status and other organs and systems.

Extraction of knowledge graph of Covid-19 through mining of unstructured biomedical corpora.

The number of biomedical articles published is increasing rapidly over the years. Currently there are about 30 million articles in PubMed and over 25 million mentions in Medline. Among these fundamentals, Biomedical Named Entity Recognition (BioNER) and Biomedical Relation Extraction (BioRE) are the most essential in analysing the literature. In the biomedical domain, Knowledge Graph is used to visualize the relationships between various entities such as proteins, chemicals and diseases. Scientific publications have increased dramatically as a result of the search for treatments and potential cures for the new Coronavirus, but efficiently analysing, integrating, and utilising related sources of information remains a difficulty. In order to effectively combat the disease during pandemics like COVID-19, literature must be used quickly and effectively. In this paper, we introduced a fully automated framework consists of BERT-BiLSTM, Knowledge graph, and Representation Learning model to extract the top diseases, chemicals, and proteins related to COVID-19 from the literature. The proposed framework uses Named Entity Recognition models for disease recognition, chemical recognition, and protein recognition. Then the system uses the Chemical - Disease Relation Extraction and Chemical - Protein Relation Extraction models. And the system extracts the entities and relations from the CORD-19 dataset using the models. The system then creates a Knowledge Graph for the extracted relations and entities. The system performs Representation Learning on this KG to get the embeddings of all entities and get the top related diseases, chemicals, and proteins with respect to COVID-19.

A broad assessment of covid-19 vaccine safety using tree-based data-mining in the vaccine safety datalink.

BACKGROUND: Except for spontaneous reporting systems, vaccine safety monitoring generally involves pre-specifying health outcomes and post-vaccination risk windows of concern. Instead, we used tree-based data-mining to look more broadly for possible adverse events after Pfizer-BioNTech, Moderna, and Janssen COVID-19 vaccination. METHODS: Vaccine Safety Datalink enrollees receiving ≥1 dose of COVID-19 vaccine in 2020-2021 were followed for 70 days after Pfizer-BioNTech or Moderna and 56 days after Janssen vaccination. Incident diagnoses in inpatient or emergency department settings were analyzed for clustering within both the hierarchical ICD-10-CM code structure and the post-vaccination follow-up period. We used the self-controlled tree-temporal scan statistic and TreeScan software. Monte Carlo simulation was used to estimate p-values; p = 0.01 was the pre-specified cut-off for statistical significance of a cluster. RESULTS: There were 4.1, 2.6, and 0.4 million Pfizer-BioNTech, Moderna, and Janssen vaccinees, respectively. Clusters after Pfizer-BioNTech vaccination included: (1) unspecified adverse effects, (2) common vaccine reactions, such as fever, myalgia, and headache, (3) myocarditis/pericarditis, and (4) less specific cardiac or respiratory symptoms, all with the strongest clusters generally after Dose 2; and (5) COVID-19/viral pneumonia/sepsis/respiratory failure in the first 3 weeks after Dose 1. Moderna results were similar but without a significant myocarditis/pericarditis cluster. Further investigation suggested the fifth signal group was a manifestation of mRNA vaccine effectiveness after the first 3 weeks. Janssen vaccinees had clusters of unspecified or common vaccine reactions, gait/mobility abnormalities, and muscle weakness. The latter two were deemed to have arisen from confounding related to practices at one site. CONCLUSIONS: We detected post-vaccination clusters of unspecified adverse effects, common vaccine reactions, and, for the mRNA vaccines, chest pain and palpitations, as well as myocarditis/pericarditis after Pfizer-BioNTech Dose 2. Unique advantages of this data mining are its untargeted nature and its inherent adjustment for the multiplicity of diagnoses and risk intervals scanned.

The Causality Inference of Public Interest in Restaurants and Bars on Daily COVID-19 Cases in the United States: Google Trends Analysis.

BACKGROUND: The COVID-19 pandemic has affected virtually every region in the world. At the time of this study, the number of daily new cases in the United States was greater than that in any other country, and the trend was increasing in most states. Google Trends provides data regarding public interest in various topics during different periods. Analyzing these trends using data mining methods may provide useful insights and observations regarding the COVID-19 outbreak. OBJECTIVE: The objective of this study is to consider the predictive ability of different search terms not directly related to COVID-19 with regard to the increase of daily cases in the United States. In particular, we are concerned with searches related to dine-in restaurants and bars. Data were obtained from the Google Trends application programming interface and the COVID-19 Tracking Project. METHODS: To test the causation of one time series on another, we used the Granger causality test. We considered the causation of two different search query trends related to dine-in restaurants and bars on daily positive cases in the US states and territories with the 10 highest and 10 lowest numbers of daily new cases of COVID-19. In addition, we used Pearson correlations to measure the linear relationships between different trends. RESULTS: Our results showed that for states and territories with higher numbers of daily cases, the historical trends in search queries related to bars and restaurants, which mainly occurred after reopening, significantly affected the number of daily new cases on average. California, for example, showed the most searches for restaurants on June 7, 2020; this affected the number of new cases within two weeks after the peak, with a P value of .004 for the Granger causality test. CONCLUSIONS: Although a limited number of search queries were considered, Google search trends for restaurants and bars showed a significant effect on daily new cases in US states and territories with higher numbers of daily new cases. We showed that these influential search trends can be used to provide additional information for prediction tasks regarding new cases in each region. These predictions can help health care leaders manage and control the impact of the COVID-19 outbreak on society and prepare for its outcomes.

Public Discourse Against Masks in the COVID-19 Era: Infodemiology Study of Twitter Data.

BACKGROUND: Despite scientific evidence supporting the importance of wearing masks to curtail the spread of COVID-19, wearing masks has stirred up a significant debate particularly on social media. OBJECTIVE: This study aimed to investigate the topics associated with the public discourse against wearing masks in the United States. We also studied the relationship between the anti-mask discourse on social media and the number of new COVID-19 cases. METHODS: We collected a total of 51,170 English tweets between January 1, 2020, and October 27, 2020, by searching for hashtags against wearing masks. We used machine learning techniques to analyze the data collected. We investigated the relationship between the volume of tweets against mask-wearing and the daily volume of new COVID-19 cases using a Pearson correlation analysis between the two-time series. RESULTS: The results and analysis showed that social media could help identify important insights related to wearing masks. The results of topic mining identified 10 categories or themes of user concerns dominated by (1) constitutional rights and freedom of choice; (2) conspiracy theory, population control, and big pharma; and (3) fake news, fake numbers, and fake pandemic. Altogether, these three categories represent almost 65% of the volume of tweets against wearing masks. The relationship between the volume of tweets against wearing masks and newly reported COVID-19 cases depicted a strong correlation wherein the rise in the volume of negative tweets led the rise in the number of new cases by 9 days. CONCLUSIONS: These findings demonstrated the potential of mining social media for understanding the public discourse about public health issues such as wearing masks during the COVID-19 pandemic. The results emphasized the relationship between the discourse on social media and the potential impact on real events such as changing the course of the pandemic. Policy makers are advised to proactively address public perception and work on shaping this perception through raising awareness, debunking negative sentiments, and prioritizing early policy intervention toward the most prevalent topics.

Changes in Doctor-Patient Relationships in China during COVID-19: A Text Mining Analysis.

Doctor-patient relationships (DPRs) in China have been straining. With the emergence of the COVID-19 pandemic, the relationships and interactions between patients and doctors are changing. This study investigated how patients' attitudes toward physicians changed during the pandemic and what factors were associated with these changes, leading to insights for improving management in the healthcare sector. This paper collected 58,600 comments regarding Chinese doctors from three regions from the online health platform Good Doctors Online (haodf.com, accessed on 13 October 2022). These comments were analyzed using text mining techniques, such as sentiment and word frequency analyses. The results showed improvements in DPRs after the pandemic, and the degree of improvement was related to the extent to which a location was affected. The findings also suggest that administrative services in the healthcare sector need further improvement. Based on these results, we summarize relevant recommendations at the end of this paper.

Tree-based data mining for safety assessment of first COVID-19 booster doses in the Vaccine Safety Datalink.

BACKGROUND: The Centers for Disease Control and Prevention's Vaccine Safety Datalink (VSD) has been performing safety surveillance for COVID-19 vaccines since their earliest authorization in the United States. Complementing its real-time surveillance for pre-specified health outcomes using pre-specified risk intervals, the VSD conducts tree-based data-mining to look for clustering of a broad range of health outcomes after COVID-19 vaccination. This study's objective was to use this untargeted, hypothesis-generating approach to assess the safety of first booster doses of Pfizer-BioNTech (BNT162b2), Moderna (mRNA-1273), and Janssen (Ad26.COV2.S) COVID-19 vaccines. METHODS: VSD enrollees receiving a first booster of COVID-19 vaccine through April 2, 2022 were followed for 56 days. Incident diagnoses in inpatient or emergency department settings were analyzed for clustering within both the hierarchical ICD-10-CM code structure and the follow-up period. The self-controlled tree-temporal scan statistic was used, conditioning on the total number of cases for each diagnosis. P-values were estimated by Monte Carlo simulation; p = 0.01 was pre-specified as the cut-off for statistical significance of clusters. RESULTS: More than 2.4 and 1.8 million subjects received Pfizer-BioNTech and Moderna boosters after an mRNA primary series, respectively. Clusters of urticaria/allergy/rash were found during Days 10-15 after the Moderna booster (p = 0.0001). Other outcomes that clustered after mRNA boosters, mostly with p = 0.0001, included unspecified adverse effects, common vaccine-associated reactions like fever and myalgia, and COVID-19. COVID-19 clusters were in Days 1-10 after booster receipt, before boosters would have become effective. There were no noteworthy clusters after boosters following primary Janssen vaccination. CONCLUSIONS: In this untargeted data-mining study of COVID-19 booster vaccination, a cluster of delayed-onset urticaria/allergy/rash was detected after the Moderna booster, as has been reported after Moderna vaccination previously. Other clusters after mRNA boosters were of unspecified or common adverse effects and COVID-19, the latter evidently reflecting immunity to COVID-19 after 10 days.