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1.
J Med Internet Res ; 23(2): e23957, 2021 02 23.
Article in English | MEDLINE | ID: covidwho-1576022

ABSTRACT

BACKGROUND: During the COVID-19 pandemic in Canada, Prime Minister Justin Trudeau provided updates on the novel coronavirus and the government's responses to the pandemic in his daily briefings from March 13 to May 22, 2020, delivered on the official Canadian Broadcasting Corporation (CBC) YouTube channel. OBJECTIVE: The aim of this study was to examine comments on Canadian Prime Minister Trudeau's COVID-19 daily briefings by YouTube users and track these comments to extract the changing dynamics of the opinions and concerns of the public over time. METHODS: We used machine learning techniques to longitudinally analyze a total of 46,732 English YouTube comments that were retrieved from 57 videos of Prime Minister Trudeau's COVID-19 daily briefings from March 13 to May 22, 2020. A natural language processing model, latent Dirichlet allocation, was used to choose salient topics among the sampled comments for each of the 57 videos. Thematic analysis was used to classify and summarize these salient topics into different prominent themes. RESULTS: We found 11 prominent themes, including strict border measures, public responses to Prime Minister Trudeau's policies, essential work and frontline workers, individuals' financial challenges, rental and mortgage subsidies, quarantine, government financial aid for enterprises and individuals, personal protective equipment, Canada and China's relationship, vaccines, and reopening. CONCLUSIONS: This study is the first to longitudinally investigate public discourse and concerns related to Prime Minister Trudeau's daily COVID-19 briefings in Canada. This study contributes to establishing a real-time feedback loop between the public and public health officials on social media. Hearing and reacting to real concerns from the public can enhance trust between the government and the public to prepare for future health emergencies.


Subject(s)
COVID-19 , Federal Government , Natural Language Processing , Public Health , Public Opinion , Social Media , COVID-19 Vaccines , Canada , Emigration and Immigration , Financial Stress , Financing, Government , Government , Humans , Longitudinal Studies , Pandemics , Personal Protective Equipment , Public Policy , Quarantine , SARS-CoV-2 , Unsupervised Machine Learning
2.
J Med Internet Res ; 23(2): e26302, 2021 02 22.
Article in English | MEDLINE | ID: covidwho-1575865

ABSTRACT

BACKGROUND: The emergence of SARS-CoV-2 (ie, COVID-19) has given rise to a global pandemic affecting 215 countries and over 40 million people as of October 2020. Meanwhile, we are also experiencing an infodemic induced by the overabundance of information, some accurate and some inaccurate, spreading rapidly across social media platforms. Social media has arguably shifted the information acquisition and dissemination of a considerably large population of internet users toward higher interactivities. OBJECTIVE: This study aimed to investigate COVID-19-related health beliefs on one of the mainstream social media platforms, Twitter, as well as potential impacting factors associated with fluctuations in health beliefs on social media. METHODS: We used COVID-19-related posts from the mainstream social media platform Twitter to monitor health beliefs. A total of 92,687,660 tweets corresponding to 8,967,986 unique users from January 6 to June 21, 2020, were retrieved. To quantify health beliefs, we employed the health belief model (HBM) with four core constructs: perceived susceptibility, perceived severity, perceived benefits, and perceived barriers. We utilized natural language processing and machine learning techniques to automate the process of judging the conformity of each tweet with each of the four HBM constructs. A total of 5000 tweets were manually annotated for training the machine learning architectures. RESULTS: The machine learning classifiers yielded areas under the receiver operating characteristic curves over 0.86 for the classification of all four HBM constructs. Our analyses revealed a basic reproduction number R0 of 7.62 for trends in the number of Twitter users posting health belief-related content over the study period. The fluctuations in the number of health belief-related tweets could reflect dynamics in case and death statistics, systematic interventions, and public events. Specifically, we observed that scientific events, such as scientific publications, and nonscientific events, such as politicians' speeches, were comparable in their ability to influence health belief trends on social media through a Kruskal-Wallis test (P=.78 and P=.92 for perceived benefits and perceived barriers, respectively). CONCLUSIONS: As an analogy of the classic epidemiology model where an infection is considered to be spreading in a population with an R0 greater than 1, we found that the number of users tweeting about COVID-19 health beliefs was amplifying in an epidemic manner and could partially intensify the infodemic. It is "unhealthy" that both scientific and nonscientific events constitute no disparity in impacting the health belief trends on Twitter, since nonscientific events, such as politicians' speeches, might not be endorsed by substantial evidence and could sometimes be misleading.


Subject(s)
COVID-19/psychology , Data Analysis , Health Education/statistics & numerical data , Machine Learning , Natural Language Processing , Public Opinion , Social Media/statistics & numerical data , COVID-19/epidemiology , Humans , Pandemics
3.
J Med Internet Res ; 23(2): e25429, 2021 02 09.
Article in English | MEDLINE | ID: covidwho-1575482

ABSTRACT

BACKGROUND: As the number of COVID-19 cases increased precipitously in the United States, policy makers and health officials marshalled their pandemic responses. As the economic impacts multiplied, anecdotal reports noted the increased use of web-based crowdfunding to defray these costs. OBJECTIVE: We examined the web-based crowdfunding response in the early stage of the COVID-19 pandemic in the United States to understand the incidence of initiation of COVID-19-related campaigns and compare them to non-COVID-19-related campaigns. METHODS: On May 16, 2020, we extracted all available data available on US campaigns that contained narratives and were created between January 1 and May 10, 2020, on GoFundMe. We identified the subset of COVID-19-related campaigns using keywords relevant to the COVID-19 pandemic. We explored the incidence of COVID-19-related campaigns by geography, by category, and over time, and we compared the characteristics of the campaigns to those of non-COVID-19-related campaigns after March 11, when the pandemic was declared. We then used a natural language processing algorithm to cluster campaigns by narrative content using overlapping keywords. RESULTS: We found that there was a substantial increase in overall GoFundMe web-based crowdfunding campaigns in March, largely attributable to COVID-19-related campaigns. However, as the COVID-19 pandemic persisted and progressed, the number of campaigns per COVID-19 case declined more than tenfold across all states. The states with the earliest disease burden had the fewest campaigns per case, indicating a lack of a case-dependent response. COVID-19-related campaigns raised more money, had a longer narrative description, and were more likely to be shared on Facebook than other campaigns in the study period. CONCLUSIONS: Web-based crowdfunding appears to be a stopgap for only a minority of campaigners. The novelty of an emergency likely impacts both campaign initiation and crowdfunding success, as it reflects the affective response of a community. Crowdfunding activity likely serves as an early signal for emerging needs and societal sentiment for communities in acute distress that could be used by governments and aid organizations to guide disaster relief and policy.


Subject(s)
COVID-19/epidemiology , Crowdsourcing/statistics & numerical data , Financial Support , COVID-19/economics , Cost of Illness , Cross-Sectional Studies , Crowdsourcing/economics , Government , Humans , Narration , Natural Language Processing , Pandemics , SARS-CoV-2 , United States/epidemiology
4.
J Med Internet Res ; 23(2): e25431, 2021 02 10.
Article in English | MEDLINE | ID: covidwho-1574637

ABSTRACT

BACKGROUND: Social media is a rich source where we can learn about people's reactions to social issues. As COVID-19 has impacted people's lives, it is essential to capture how people react to public health interventions and understand their concerns. OBJECTIVE: We aim to investigate people's reactions and concerns about COVID-19 in North America, especially in Canada. METHODS: We analyzed COVID-19-related tweets using topic modeling and aspect-based sentiment analysis (ABSA), and interpreted the results with public health experts. To generate insights on the effectiveness of specific public health interventions for COVID-19, we compared timelines of topics discussed with the timing of implementation of interventions, synergistically including information on people's sentiment about COVID-19-related aspects in our analysis. In addition, to further investigate anti-Asian racism, we compared timelines of sentiments for Asians and Canadians. RESULTS: Topic modeling identified 20 topics, and public health experts provided interpretations of the topics based on top-ranked words and representative tweets for each topic. The interpretation and timeline analysis showed that the discovered topics and their trend are highly related to public health promotions and interventions such as physical distancing, border restrictions, handwashing, staying home, and face coverings. After training the data using ABSA with human-in-the-loop, we obtained 545 aspect terms (eg, "vaccines," "economy," and "masks") and 60 opinion terms such as "infectious" (negative) and "professional" (positive), which were used for inference of sentiments of 20 key aspects selected by public health experts. The results showed negative sentiments related to the overall outbreak, misinformation and Asians, and positive sentiments related to physical distancing. CONCLUSIONS: Analyses using natural language processing techniques with domain expert involvement can produce useful information for public health. This study is the first to analyze COVID-19-related tweets in Canada in comparison with tweets in the United States by using topic modeling and human-in-the-loop domain-specific ABSA. This kind of information could help public health agencies to understand public concerns as well as what public health messages are resonating in our populations who use Twitter, which can be helpful for public health agencies when designing a policy for new interventions.


Subject(s)
Attitude to Health , COVID-19 , Public Health , Racism , Social Media , Canada , Disease Outbreaks , Humans , Natural Language Processing , North America , SARS-CoV-2 , United States
5.
J Med Internet Res ; 23(2): e20545, 2021 02 19.
Article in English | MEDLINE | ID: covidwho-1573803

ABSTRACT

COVID-19 cases are exponentially increasing worldwide; however, its clinical phenotype remains unclear. Natural language processing (NLP) and machine learning approaches may yield key methods to rapidly identify individuals at a high risk of COVID-19 and to understand key symptoms upon clinical manifestation and presentation. Data on such symptoms may not be accurately synthesized into patient records owing to the pressing need to treat patients in overburdened health care settings. In this scenario, clinicians may focus on documenting widely reported symptoms that indicate a confirmed diagnosis of COVID-19, albeit at the expense of infrequently reported symptoms. While NLP solutions can play a key role in generating clinical phenotypes of COVID-19, they are limited by the resulting limitations in data from electronic health records (EHRs). A comprehensive record of clinic visits is required-audio recordings may be the answer. A recording of clinic visits represents a more comprehensive record of patient-reported symptoms. If done at scale, a combination of data from the EHR and recordings of clinic visits can be used to power NLP and machine learning models, thus rapidly generating a clinical phenotype of COVID-19. We propose the generation of a pipeline extending from audio or video recordings of clinic visits to establish a model that factors in clinical symptoms and predict COVID-19 incidence. With vast amounts of available data, we believe that a prediction model can be rapidly developed to promote the accurate screening of individuals at a high risk of COVID-19 and to identify patient characteristics that predict a greater risk of a more severe infection. If clinical encounters are recorded and our NLP model is adequately refined, benchtop virologic findings would be better informed. While clinic visit recordings are not the panacea for this pandemic, they are a low-cost option with many potential benefits, which have recently begun to be explored.


Subject(s)
Ambulatory Care/standards , COVID-19/genetics , Communications Media/standards , Electronic Health Records/standards , Machine Learning/standards , Natural Language Processing , Humans , Phenotype , SARS-CoV-2
6.
Comput Math Methods Med ; 2021: 1546343, 2021.
Article in English | MEDLINE | ID: covidwho-1574507

ABSTRACT

As the COVID-19 pandemic continues, the need for a better health care facility is highlighted more than ever. Besides physical health, mental health conditions have become a significant concern. Unfortunately, there are few opportunities for people to receive mental health care. There are inadequate facilities for seeking mental health support even in big cities, let alone remote areas. This paper presents the structure and implementation procedures for a mental health support system combining technology and professionals. The system is a web platform where mental health seekers can register and use functionalities like NLP-based chatbot for personality assessment, chatting with like-minded people, and one-to-one video conferencing with a mental health professional. The video calling feature of the system has emotion detection capabilities using computer vision. The system also includes downloadable prescription facilities and a payment gateway for secure transactions. From a technological aspect, the conversational NLP-based chatbot and computer vision-powered video calling are the system's most important features. The system has a documentation facility to analyze the mental health condition over time. The web platform is built using React.js for the frontend and Express.js for the backend. MongoDB is used as the database of the platform. The NLP chatbot is built on a three-layered deep neural network model that is programmed in the Python language and uses the NLTK, TensorFlow, and Keras sequential API. Video conference is one of the most important features of the platform. To create the video calling feature, Express.js, Socket.io, and Socket.io-client have been used. The emotion detection feature is implemented on video conferences using computer vision, Haar Cascade, and TensorFlow. All the implemented features are tested and work fine. The targeted users for the platform are teenagers, youth, and the middle-aged population. Mental health-seeking is still considered taboo in some societies today. Apart from basic established facilities, this social dilemma of undergoing treatment for mental health is causing severe damage to individuals. A solution to this problem can be a remote platform for mental health support. With this goal in mind, this system is designed to provide mental health support to people remotely from anywhere worldwide.


Subject(s)
Mental Health , Software , Telemedicine , Humans , Internet , Natural Language Processing , User-Computer Interface , Videoconferencing
7.
BMJ Open ; 11(11): e056601, 2021 11 05.
Article in English | MEDLINE | ID: covidwho-1504533

ABSTRACT

OBJECTIVES: Online health forums provide rich and untapped real-time data on population health. Through novel data extraction and natural language processing (NLP) techniques, we characterise the evolution of mental and physical health concerns relating to the COVID-19 pandemic among online health forum users. SETTING AND DESIGN: We obtained data from three leading online health forums: HealthBoards, Inspire and HealthUnlocked, from the period 1 January 2020 to 31 May 2020. Using NLP, we analysed the content of posts related to COVID-19. PRIMARY OUTCOME MEASURES: (1) Proportion of forum posts containing COVID-19 keywords; (2) proportion of forum users making their very first post about COVID-19; (3) proportion of COVID-19-related posts containing content related to physical and mental health comorbidities. RESULTS: Data from 739 434 posts created by 53 134 unique users were analysed. A total of 35 581 posts (4.8%) contained a COVID-19 keyword. Posts discussing COVID-19 and related comorbid disorders spiked in early March to mid-March around the time of global implementation of lockdowns prompting a large number of users to post on online health forums for the first time. Over a quarter of COVID-19-related thread titles mentioned a physical or mental health comorbidity. CONCLUSIONS: We demonstrate that it is feasible to characterise the content of online health forum user posts regarding COVID-19 and measure changes over time. The pandemic and corresponding public response has had a significant impact on posters' queries regarding mental health. Social media data sources such as online health forums can be harnessed to strengthen population-level mental health surveillance.


Subject(s)
COVID-19 , Social Media , Communicable Disease Control , Humans , Natural Language Processing , Pandemics , SARS-CoV-2
8.
BMJ Glob Health ; 6(4)2021 04.
Article in English | MEDLINE | ID: covidwho-1476462

ABSTRACT

INTRODUCTION: Experts agree that male involvement in maternal health is a multifaceted concept, but a robust assessment is lacking, hampering interpretation of the literature. This systematic review aims to examine the conceptualisation of male involvement in maternal health globally and review commonly used indicators. METHODS: PubMed, Embase, Scopus, Web of Science and CINAHL databases were searched for quantitative literature (between the years 2000 and 2020) containing indicators representing male involvement in maternal health, which was defined as the involvement, participation, engagement or support of men in all activities related to maternal health. RESULTS: After full-text review, 282 studies were included in the review. Most studies were conducted in Africa (43%), followed by North America (23%), Asia (15%) and Europe (12%). Descriptive and text mining analysis showed male involvement has been conceptualised by focusing on two main aspects: psychosocial support and instrumental support for maternal health care utilisation. Differences in measurement and topics were noted according to continent with Africa focusing on HIV prevention, North America and Europe on psychosocial health and stress, and Asia on nutrition. One-third of studies used one single indicator and no common pattern of indicators could be identified. Antenatal care attendance was the most used indicator (40%), followed by financial support (17%), presence during childbirth (17%) and HIV testing (14%). Majority of studies did not collect data from men directly. DISCUSSION: Researchers often focus on a single aspect of male involvement, resulting in a narrow set of indicators. Aspects such as communication, shared decision making and the subjective feeling of support have received little attention. We believe a broader holistic scope can broaden the potential of male involvement programmes and stimulate a gender-transformative approach. Further research is recommended to develop a robust and comprehensive set of indicators for assessing male involvement in maternal health.


Subject(s)
Maternal Health Services , Maternal Health , Africa , Europe , Female , Humans , Male , Natural Language Processing , Pregnancy
9.
Yearb Med Inform ; 30(1): 257-263, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1392950

ABSTRACT

OBJECTIVES: To analyze the content of publications within the medical NLP domain in 2020. METHODS: Automatic and manual preselection of publications to be reviewed, and selection of the best NLP papers of the year. Analysis of the important issues. RESULTS: Three best papers have been selected in 2020. We also propose an analysis of the content of the NLP publications in 2020, all topics included. CONCLUSION: The two main issues addressed in 2020 are related to the investigation of COVID-related questions and to the further adaptation and use of transformer models. Besides, the trends from the past years continue, such as diversification of languages processed and use of information from social networks.


Subject(s)
COVID-19 , Natural Language Processing , Social Networking , Clinical Trials as Topic , Humans , Medical Records , Mental Disorders
10.
JMIR Public Health Surveill ; 7(8): e29029, 2021 08 17.
Article in English | MEDLINE | ID: covidwho-1360690

ABSTRACT

BACKGROUND: Widespread fear surrounding COVID-19, coupled with physical and social distancing orders, has caused severe adverse mental health outcomes. Little is known, however, about how the COVID-19 crisis has impacted LGBTQ+ youth, who disproportionately experienced a high rate of adverse mental health outcomes before the COVID-19 pandemic. OBJECTIVE: We aimed to address this knowledge gap by harnessing natural language processing methodologies to investigate the evolution of conversation topics in the most popular subreddit for LGBTQ+ youth. METHODS: We generated a data set of all r/LGBTeens subreddit posts (n=39,389) between January 1, 2020 and February 1, 2021 and analyzed meaningful trends in anxiety, anger, and sadness in the posts. Because the distribution of anxiety before widespread social distancing orders was meaningfully different from the distribution after (P<.001), we employed latent Dirichlet allocation to examine topics that provoked this shift in anxiety. RESULTS: We did not find any differences in LGBTQ+ youth anger and sadness before and after government-mandated social distancing; however, anxiety increased significantly (P<.001). Further analysis revealed a list of 10 anxiety-provoking topics discussed during the pandemic: attraction to a friend, coming out, coming out to family, discrimination, education, exploring sexuality, gender pronouns, love and relationship advice, starting a new relationship, and struggling with mental health. CONCLUSIONS: During the COVID-19 pandemic, LGBTQ+ teens increased their reliance on anonymous discussion forums when discussing anxiety-provoking topics. LGBTQ+ teens likely perceived anonymous forums as safe spaces for discussing lifestyle stressors during COVID-19 disruptions (eg, school closures). The list of prevalent anxiety-provoking topics in LGBTQ+ teens' anonymous discussions can inform future mental health interventions in LGBTQ+ youth.


Subject(s)
Anxiety/epidemiology , COVID-19/psychology , Natural Language Processing , Pandemics , Sexual and Gender Minorities/psychology , Social Media/statistics & numerical data , Social Media/trends , Adolescent , COVID-19/epidemiology , Emotions , Female , Humans , Longitudinal Studies , Male , Sexual and Gender Minorities/statistics & numerical data
11.
Annu Rev Biomed Data Sci ; 4: 313-339, 2021 07 20.
Article in English | MEDLINE | ID: covidwho-1346098

ABSTRACT

The COVID-19 (coronavirus disease 2019) pandemic has had a significant impact on society, both because of the serious health effects of COVID-19 and because of public health measures implemented to slow its spread. Many of these difficulties are fundamentally information needs; attempts to address these needs have caused an information overload for both researchers and the public. Natural language processing (NLP)-the branch of artificial intelligence that interprets human language-can be applied to address many of the information needs made urgent by the COVID-19 pandemic. This review surveys approximately 150 NLP studies and more than 50 systems and datasets addressing the COVID-19 pandemic. We detail work on four core NLP tasks: information retrieval, named entity recognition, literature-based discovery, and question answering. We also describe work that directly addresses aspects of the pandemic through four additional tasks: topic modeling, sentiment and emotion analysis, caseload forecasting, and misinformation detection. We conclude by discussing observable trends and remaining challenges.


Subject(s)
COVID-19/epidemiology , Information Storage and Retrieval/methods , Natural Language Processing , Communication , Data Mining/methods , Datasets as Topic , Emotions , Humans , Knowledge Discovery , Pandemics , Periodicals as Topic , Software
12.
J Med Internet Res ; 23(7): e26995, 2021 07 16.
Article in English | MEDLINE | ID: covidwho-1341580

ABSTRACT

BACKGROUND: Papers on COVID-19 are being published at a high rate and concern many different topics. Innovative tools are needed to aid researchers to find patterns in this vast amount of literature to identify subsets of interest in an automated fashion. OBJECTIVE: We present a new online software resource with a friendly user interface that allows users to query and interact with visual representations of relationships between publications. METHODS: We publicly released an application called PLATIPUS (Publication Literature Analysis and Text Interaction Platform for User Studies) that allows researchers to interact with literature supplied by COVIDScholar via a visual analytics platform. This tool contains standard filtering capabilities based on authors, journals, high-level categories, and various research-specific details via natural language processing and dozens of customizable visualizations that dynamically update from a researcher's query. RESULTS: PLATIPUS is available online and currently links to over 100,000 publications and is still growing. This application has the potential to transform how COVID-19 researchers use public literature to enable their research. CONCLUSIONS: The PLATIPUS application provides the end user with a variety of ways to search, filter, and visualize over 100,00 COVID-19 publications.


Subject(s)
COVID-19 , Image Interpretation, Computer-Assisted , Information Storage and Retrieval , SARS-CoV-2 , Humans , Natural Language Processing , Software , User-Computer Interface
13.
PLoS One ; 16(7): e0254359, 2021.
Article in English | MEDLINE | ID: covidwho-1308179

ABSTRACT

Reopening amid the COVID-19 pandemic has triggered a battle on social media. The supporters perceived that the lockdown policy could damage the economy and exacerbate social inequality. By contrast, the opponents believed it was necessary to contain the spread and ensure a safe environment for recovery. Anatomy into the battle is of importance to address public concerns, beliefs, and values, thereby enabling policymakers to determine the appropriate solutions to implement reopening policy. To this end, we investigated over 1.5 million related Twitter postings from April 17 to May 30, 2020. With the aid of natural language processing (NLP) techniques and machine learning classifiers, we classified each tweet into either a "supporting" or "opposing" class and then investigated the public perception from temporal and spatial perspectives. From the temporal dimension, we found that both political and scientific news that were extensively discussed on Twitter led to the perception of opposing reopening. Further, being the first mover with full reopen adversely affected the public reaction to reopening policy, while being the follower or late mover resulted in positive responses. From the spatial dimension, the correlation and regression analyses suggest that the state-level perception was very likely to be associated with political affiliation and health value.


Subject(s)
Attitude , COVID-19/psychology , Quarantine/psychology , Social Media/statistics & numerical data , COVID-19/epidemiology , COVID-19/prevention & control , Humans , Machine Learning , Natural Language Processing , Politics , Spatio-Temporal Analysis
14.
J Med Internet Res ; 23(6): e24435, 2021 06 29.
Article in English | MEDLINE | ID: covidwho-1286902

ABSTRACT

BACKGROUND: Vaccination is a cornerstone of the prevention of communicable infectious diseases; however, vaccines have traditionally met with public fear and hesitancy, and COVID-19 vaccines are no exception. Social media use has been demonstrated to play a role in the low acceptance of vaccines. OBJECTIVE: The aim of this study is to identify the topics and sentiments in the public COVID-19 vaccine-related discussion on social media and discern the salient changes in topics and sentiments over time to better understand the public perceptions, concerns, and emotions that may influence the achievement of herd immunity goals. METHODS: Tweets were downloaded from a large-scale COVID-19 Twitter chatter data set from March 11, 2020, the day the World Health Organization declared COVID-19 a pandemic, to January 31, 2021. We used R software to clean the tweets and retain tweets that contained the keywords vaccination, vaccinations, vaccine, vaccines, immunization, vaccinate, and vaccinated. The final data set included in the analysis consisted of 1,499,421 unique tweets from 583,499 different users. We used R to perform latent Dirichlet allocation for topic modeling as well as sentiment and emotion analysis using the National Research Council of Canada Emotion Lexicon. RESULTS: Topic modeling of tweets related to COVID-19 vaccines yielded 16 topics, which were grouped into 5 overarching themes. Opinions about vaccination (227,840/1,499,421 tweets, 15.2%) was the most tweeted topic and remained a highly discussed topic during the majority of the period of our examination. Vaccine progress around the world became the most discussed topic around August 11, 2020, when Russia approved the world's first COVID-19 vaccine. With the advancement of vaccine administration, the topic of instruction on getting vaccines gradually became more salient and became the most discussed topic after the first week of January 2021. Weekly mean sentiment scores showed that despite fluctuations, the sentiment was increasingly positive in general. Emotion analysis further showed that trust was the most predominant emotion, followed by anticipation, fear, sadness, etc. The trust emotion reached its peak on November 9, 2020, when Pfizer announced that its vaccine is 90% effective. CONCLUSIONS: Public COVID-19 vaccine-related discussion on Twitter was largely driven by major events about COVID-19 vaccines and mirrored the active news topics in mainstream media. The discussion also demonstrated a global perspective. The increasingly positive sentiment around COVID-19 vaccines and the dominant emotion of trust shown in the social media discussion may imply higher acceptance of COVID-19 vaccines compared with previous vaccines.


Subject(s)
COVID-19 Vaccines , COVID-19 , Emotions , Latent Class Analysis , Social Media , Trust , Vaccination/psychology , COVID-19/immunology , COVID-19/prevention & control , COVID-19 Vaccines/administration & dosage , COVID-19 Vaccines/adverse effects , COVID-19 Vaccines/immunology , Humans , Immunity, Herd , Natural Language Processing , Pandemics , SARS-CoV-2/immunology
15.
J Med Internet Res ; 23(7): e26995, 2021 07 16.
Article in English | MEDLINE | ID: covidwho-1278292

ABSTRACT

BACKGROUND: Papers on COVID-19 are being published at a high rate and concern many different topics. Innovative tools are needed to aid researchers to find patterns in this vast amount of literature to identify subsets of interest in an automated fashion. OBJECTIVE: We present a new online software resource with a friendly user interface that allows users to query and interact with visual representations of relationships between publications. METHODS: We publicly released an application called PLATIPUS (Publication Literature Analysis and Text Interaction Platform for User Studies) that allows researchers to interact with literature supplied by COVIDScholar via a visual analytics platform. This tool contains standard filtering capabilities based on authors, journals, high-level categories, and various research-specific details via natural language processing and dozens of customizable visualizations that dynamically update from a researcher's query. RESULTS: PLATIPUS is available online and currently links to over 100,000 publications and is still growing. This application has the potential to transform how COVID-19 researchers use public literature to enable their research. CONCLUSIONS: The PLATIPUS application provides the end user with a variety of ways to search, filter, and visualize over 100,00 COVID-19 publications.


Subject(s)
COVID-19 , Image Interpretation, Computer-Assisted , Information Storage and Retrieval , SARS-CoV-2 , Humans , Natural Language Processing , Software , User-Computer Interface
16.
J Med Internet Res ; 23(6): e26655, 2021 06 21.
Article in English | MEDLINE | ID: covidwho-1259299

ABSTRACT

BACKGROUND: COVID-19 has continued to spread in the United States and globally. Closely monitoring public engagement and perceptions of COVID-19 and preventive measures using social media data could provide important information for understanding the progress of current interventions and planning future programs. OBJECTIVE: The aim of this study is to measure the public's behaviors and perceptions regarding COVID-19 and its effects on daily life during 5 months of the pandemic. METHODS: Natural language processing (NLP) algorithms were used to identify COVID-19-related and unrelated topics in over 300 million online data sources from June 15 to November 15, 2020. Posts in the sample were geotagged by NetBase, a third-party data provider, and sensitivity and positive predictive value were both calculated to validate the classification of posts. Each post may have included discussion of multiple topics. The prevalence of discussion regarding these topics was measured over this time period and compared to daily case rates in the United States. RESULTS: The final sample size included 9,065,733 posts, 70% of which were sourced from the United States. In October and November, discussion including mentions of COVID-19 and related health behaviors did not increase as it had from June to September, despite an increase in COVID-19 daily cases in the United States beginning in October. Additionally, discussion was more focused on daily life topics (n=6,210,255, 69%), compared with COVID-19 in general (n=3,390,139, 37%) and COVID-19 public health measures (n=1,836,200, 20%). CONCLUSIONS: There was a decline in COVID-19-related social media discussion sourced mainly from the United States, even as COVID-19 cases in the United States increased to the highest rate since the beginning of the pandemic. Targeted public health messaging may be needed to ensure engagement in public health prevention measures as global vaccination efforts continue.


Subject(s)
COVID-19/epidemiology , Public Health/statistics & numerical data , Social Media/statistics & numerical data , Cross-Sectional Studies , Humans , Natural Language Processing , Pandemics , SARS-CoV-2 , United States/epidemiology , Vaccination
17.
J Am Med Inform Assoc ; 28(8): 1765-1776, 2021 07 30.
Article in English | MEDLINE | ID: covidwho-1246728

ABSTRACT

OBJECTIVE: To utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online. MATERIALS AND METHODS: We developed a distributed, federated network of 12 health systems that harmonized their EHRs and submitted aggregate answers to consortia questions posted at https://www.covid19questions.org. Our consortium developed processes and implemented distributed algorithms to produce answers to a variety of questions. We were able to generate counts, descriptive statistics, and build a multivariate, iterative regression model without centralizing individual-level data. RESULTS: Our public website contains answers to various clinical questions, a web form for users to ask questions in natural language, and a list of items that are currently pending responses. The results show, for example, that patients who were taking angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers, within the year before admission, had lower unadjusted in-hospital mortality rates. We also showed that, when adjusted for, age, sex, and ethnicity were not significantly associated with mortality. We demonstrated that it is possible to answer questions about COVID-19 using EHR data from systems that have different policies and must follow various regulations, without moving data out of their health systems. DISCUSSION AND CONCLUSIONS: We present an alternative or a complement to centralized COVID-19 registries of EHR data. We can use multivariate distributed logistic regression on observations recorded in the process of care to generate results without transferring individual-level data outside the health systems.


Subject(s)
Algorithms , COVID-19 , Computer Communication Networks , Confidentiality , Electronic Health Records , Information Storage and Retrieval/methods , Natural Language Processing , Common Data Elements , Female , Humans , Logistic Models , Male , Registries
18.
Brief Funct Genomics ; 20(3): 181-195, 2021 06 09.
Article in English | MEDLINE | ID: covidwho-1246686

ABSTRACT

With the development of high-throughput sequencing technology, biological sequence data reflecting life information becomes increasingly accessible. Particularly on the background of the COVID-19 pandemic, biological sequence data play an important role in detecting diseases, analyzing the mechanism and discovering specific drugs. In recent years, pretraining models that have emerged in natural language processing have attracted widespread attention in many research fields not only to decrease training cost but also to improve performance on downstream tasks. Pretraining models are used for embedding biological sequence and extracting feature from large biological sequence corpus to comprehensively understand the biological sequence data. In this survey, we provide a broad review on pretraining models for biological sequence data. Moreover, we first introduce biological sequences and corresponding datasets, including brief description and accessible link. Subsequently, we systematically summarize popular pretraining models for biological sequences based on four categories: CNN, word2vec, LSTM and Transformer. Then, we present some applications with proposed pretraining models on downstream tasks to explain the role of pretraining models. Next, we provide a novel pretraining scheme for protein sequences and a multitask benchmark for protein pretraining models. Finally, we discuss the challenges and future directions in pretraining models for biological sequences.


Subject(s)
Algorithms , Computational Biology/methods , Data Mining/methods , High-Throughput Nucleotide Sequencing/methods , Natural Language Processing , Software , Datasets as Topic , Deep Learning , Humans , Models, Theoretical
19.
Am J Emerg Med ; 49: 52-57, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1244700

ABSTRACT

PURPOSE: During the COVID-19 pandemic, emergency department (ED) volumes have fluctuated. We hypothesized that natural language processing (NLP) models could quantify changes in detection of acute abdominal pathology (acute appendicitis (AA), acute diverticulitis (AD), or bowel obstruction (BO)) on CT reports. METHODS: This retrospective study included 22,182 radiology reports from CT abdomen/pelvis studies performed at an urban ED between January 1, 2018 to August 14, 2020. Using a subset of 2448 manually annotated reports, we trained random forest NLP models to classify the presence of AA, AD, and BO in report impressions. Performance was assessed using 5-fold cross validation. The NLP classifiers were then applied to all reports. RESULTS: The NLP classifiers for AA, AD, and BO demonstrated cross-validation classification accuracies between 0.97 and 0.99 and F1-scores between 0.86 and 0.91. When applied to all CT reports, the estimated numbers of AA, AD, and BO cases decreased 43-57% in April 2020 (first regional peak of COVID-19 cases) compared to 2018-2019. However, the number of abdominal pathologies detected rebounded in May-July 2020, with increases above historical averages for AD. The proportions of CT studies with these pathologies did not significantly increase during the pandemic period. CONCLUSION: Dramatic decreases in numbers of acute abdominal pathologies detected by ED CT studies were observed early on during the COVID-19 pandemic, though these numbers rapidly rebounded. The proportions of CT cases with these pathologies did not increase, which suggests patients deferred care during the first pandemic peak. NLP can help automatically track findings in ED radiology reporting.


Subject(s)
Appendicitis/diagnostic imaging , Diverticulitis/diagnostic imaging , Emergency Service, Hospital , Intestinal Obstruction/diagnostic imaging , Tomography, X-Ray Computed/statistics & numerical data , Abdomen/diagnostic imaging , COVID-19/epidemiology , Humans , Massachusetts/epidemiology , Natural Language Processing , Retrospective Studies , SARS-CoV-2 , Utilization Review
20.
J Community Psychol ; 49(6): 1718-1731, 2021 08.
Article in English | MEDLINE | ID: covidwho-1231854

ABSTRACT

Large amounts of text-based data, like study abstracts, often go unanalyzed because the task is laborious. Natural language processing (NLP) uses computer-based algorithms not traditionally implemented in community psychology to effectively and efficiently process text. These methods include examining the frequency of words and phrases, the clustering of topics, and the interrelationships of words. This article applied NLP to explore the concept of equity in community psychology. The COVID-19 crisis has made pre-existing health equity gaps even more salient. Community psychology has a specific interest in working with organizations, systems, and communities to address social determinants that perpetuate inequities by refocusing interventions around achieving health and wellness for all. This article examines how community psychology has discussed equity thus far to identify strengths and gaps for future research and practice. The results showed the prominence of community-based participatory research and the diversity of settings researchers work in. However, the total number of abstracts with equity concepts was lower than expected, which suggests there is a need for a continued focus on equity.


Subject(s)
Community Psychiatry/methods , Community-Based Participatory Research/methods , Health Equity/statistics & numerical data , Knowledge Discovery/methods , Natural Language Processing , Social Determinants of Health/statistics & numerical data , Humans , Periodicals as Topic
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