Adverse event detection by integrating twitter data and VAERS

Background: Vaccinehasbeenoneofthemostsuccessfulpublichealthinterventionstodate.However,vaccines are pharmaceutical products that carry risks so that many adverse events (AEs) are reported after receiving vaccines. Traditional adverse event reporting systems suffer from several crucial challenges including poor timeliness. This motivates increasing social media-based detection systems, which demonstrate successful capability to capture timely and prevalent disease information. Despite these advantages, social media-based AE detection suffers from serious challenges such as labor-intensive labeling and class imbalance of the training data. Results: Totacklebothchallengesfromtraditionalreportingsystemsandsocialmedia,weexploittheircomplementary strength and develop a combinatorial classification approach by integrating Twitter data and the Vaccine Adverse Event Reporting System (VAERS) information aiming to identify potential AEs after influenza vaccine. Specifically, we combine formal reports which have accurately predefined labels with social media data to reduce the cost of manual labeling; in order to combat the class imbalance problem, a max-rule based multi-instance learning method is proposed to bias positive users. Various experiments were conducted to validate our model compared with other baselines. We observed that (1) multi-instance learning methods outperformed baselines when only Twitter data were used; (2) formal reports helped improve the performance metrics of our multi-instance learning methods consistently while affecting the performance of other baselines negatively; (3) the effect of formal reports was more obvious when the training size was smaller. Case studies show that our model labeled users and tweets accurately. Conclusions: WehavedevelopedaframeworktodetectvaccineAEsbycombiningformalreportswithsocialmedia data. We demonstrate the power of formal reports on the performance improvement of AE detection when the amount of social media data was small. Various experiments and case studies show the effectiveness of our model

Mild Adverse Events of Sputnik V Vaccine in Russia: Social Media Content Analysis of Telegram via Deep Learning

Background: There is a limited amount of data on the safety profile of the COVID-19 vector vaccine Gam-COVID-Vac (Sputnik V). Previous infodemiology studies showed that social media discourse could be analyzed to assess the most concerning adverse events (AE) caused by drugs. Objective: We aimed to investigate mild AEs of Sputnik V based on a participatory trial conducted on Telegram in the Russian language. We compared AEs extracted from Telegram with other limited databases on Sputnik V and other COVID-19 vaccines. We explored symptom co-occurrence patterns and determined how counts of administered doses, age, gender, and sequence of shots could confound the reporting of AEs. Methods: We collected a unique dataset consisting of 11,515 self-reported Sputnik V vaccine AEs posted on the Telegram group, and we utilized natural language processing methods to extract AEs. Specifically, we performed multilabel classifications using the deep neural language model Bidirectional Encoder Representations from Transformers (BERT) “DeepPavlov,” which was pretrained on a Russian language corpus and applied to the Telegram messages. The resulting area under the curve score was 0.991. We chose symptom classes that represented the following AEs: fever, pain, chills, fatigue, nausea/vomiting, headache, insomnia, lymph node enlargement, erythema, pruritus, swelling, and diarrhea. Results: Telegram users complained mostly about pain (5461/11,515, 47.43%), fever (5363/11,515, 46.57%), fatigue (3862/11,515, 33.54%), and headache (2855/11,515, 24.79%). Women reported more AEs than men (1.2-fold, P<.001). In addition, there were more AEs from the first dose than from the second dose (1.1-fold, P<.001), and the number of AEs decreased with age (β=.05 per year, P<.001). The results also showed that Sputnik V AEs were more similar to other vector vaccines (132 units) than with messenger RNA vaccines (241 units) according to the average Euclidean distance between the vectors of AE frequencies. Elderly Telegram users reported significantly more (5.6-fold on average) systemic AEs than their peers, according to the results of the phase 3 clinical trials published in The Lancet. However, the AEs reported in Telegram posts were consistent (Pearson correlation r=0.94, P=.02) with those reported in the Argentinian postmarketing AE registry. Conclusions: After the Sputnik V vaccination, Russian Telegram users reported mostly pain, fever, and fatigue. The Sputnik V AE profile was comparable with other vector COVID-19 vaccines. Discussion on social media could provide meaningful information about the AE profile of novel vaccines

Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches

Drug Safety (DS) is a domain with significant public health and social impact. Knowledge Engineering (KE) is the Computer Science discipline elaborating on methods and tools for developing “knowledge-intensive” systems, depending on a conceptual “knowledge” schema and some kind of “reasoning” process. The present systematic and mapping review aims to investigate KE-based approaches employed for DS and highlight the introduced added value as well as trends and possible gaps in the domain. Journal articles published between 2006 and 2017 were retrieved from PubMed/MEDLINE and Web of Science® (873 in total) and filtered based on a comprehensive set of inclusion/exclusion criteria. The 80 finally selected articles were reviewed on full-text, while the mapping process relied on a set of concrete criteria (concerning specific KE and DS core activities, special DS topics, employed data sources, reference ontologies/terminologies, and computational methods, etc.). The analysis results are publicly available as online interactive analytics graphs. The review clearly depicted increased use of KE approaches for DS. The collected data illustrate the use of KE for various DS aspects, such as Adverse Drug Event (ADE) information collection, detection, and assessment. Moreover, the quantified analysis of using KE for the respective DS core activities highlighted room for intensifying research on KE for ADE monitoring, prevention and reporting. Finally, the assessed use of the various data sources for DS special topics demonstrated extensive use of dominant data sources for DS surveillance, i.e., Spontaneous Reporting Systems, but also increasing interest in the use of emerging data sources, e.g., observational healthcare databases, biochemical/genetic databases, and social media. Various exemplar applications were identified with promising results, e.g., improvement in Adverse Drug Reaction (ADR) prediction, detection of drug interactions, and novel ADE profiles related with specific mechanisms of action, etc. Nevertheless, since the reviewed studies mostly concerned proof-of-concept implementations, more intense research is required to increase the maturity level that is necessary for KE approaches to reach routine DS practice. In conclusion, we argue that efficiently addressing DS data analytics and management challenges requires the introduction of high-throughput KE-based methods for effective knowledge discovery and management, resulting ultimately, in the establishment of a continuous learning DS system

It's Not All About Autism: the Emerging Landscape of Anti-Vaccination Sentiment on Facebook

Introduction: The anti-vaccination movement has been present since the early 1700s. Previous research suggests that social media may be fueling the spread of anti-vaccination messaging. Therefore, the purpose of this thesis is to (1) highlight major events in the history of the anti-vaccine movement, (2) present a stand-alone journal article from a systematic analysis of individuals known to express anti-vaccination sentiment on Facebook, and (3) integrate the conclusions presented in the article into a broader historical framework. Methods: A literature review was conducted for the historical overview. For the journal article, our data set consisted of 197 individuals with Facebook accounts who posted anti-vaccination comments on a prominent local pediatric clinic’s Facebook page. For each individual, we systematically analyzed publicly available content using quantitative coding, descriptive analysis, social network analysis, and an in-depth qualitative assessment. Results: Throughout history, the anti-vaccination movement has consistently sued fiery rhetoric and vivid imagery to spread its messages, which often center on concerns of liberty and safety. Analysis of Facebook profiles found that more individuals posted content related to mistrust in the medical community, liberty, and belief in homeopathic remedies compared to those who posted that vaccines cause autism. Among 136 individuals who divulged their location, 36 states and 8 countries outside the U.S. were represented. In a 2-mode network of individuals and topics, modularity analysis revealed 4 distinct sub-groups: (1) liberty, (2) naturalness, (3) illness, and (4) conspiracy. Qualitative analysis found that individuals often share posts from Facebook groups that market themselves as pro-science. Conclusion: Individuals on Facebook frequently posted anti-vaccine content that echoed historical concerns. Our findings suggest social media outlets facilitate anti-vaccination connection and organization, thus assisting in the amplification and diffusion of centuries’ old arguments and techniques. These findings are significant for public health in that they will inform the development of updated messaging around vaccination, and suggest the importance of understanding the history of the anti-vaccination movement when developing these messages. These findings also suggest a valuable opportunity for public health practitioners to leverage social networks to deliver more effective, tailored interventions to different constituencies

약물 감시를 위한 비정형 텍스트 내 임상 정보 추출 연구

학위논문(박사) -- 서울대학교대학원 : 융합과학기술대학원 응용바이오공학과, 2023. 2. 이형기.Pharmacovigilance is a scientific activity to detect, evaluate and understand the occurrence of adverse drug events or other problems related to drug safety. However, concerns have been raised over the quality of drug safety information for pharmacovigilance, and there is also a need to secure a new data source to acquire drug safety information. On the other hand, the rise of pre-trained language models based on a transformer architecture has accelerated the application of natural language processing (NLP) techniques in diverse domains. In this context, I tried to define two problems in pharmacovigilance as an NLP task and provide baseline models for the defined tasks: 1) extracting comprehensive drug safety information from adverse drug events narratives reported through a spontaneous reporting system (SRS) and 2) extracting drug-food interaction information from abstracts of biomedical articles. I developed annotation guidelines and performed manual annotation, demonstrating that strong NLP models can be trained to extracted clinical information from unstructrued free-texts by fine-tuning transformer-based language models on a high-quality annotated corpus. Finally, I discuss issues to consider when when developing annotation guidelines for extracting clinical information related to pharmacovigilance. The annotated corpora and the NLP models in this dissertation can streamline pharmacovigilance activities by enhancing the data quality of reported drug safety information and expanding the data sources.약물 감시는 약물 부작용 또는 약물 안전성과 관련된 문제의 발생을 감지, 평가 및 이해하기 위한 과학적 활동이다. 그러나 약물 감시에 사용되는 의약품 안전성 정보의 보고 품질에 대한 우려가 꾸준히 제기되었으며, 해당 보고 품질을 높이기 위해서는 안전성 정보를 확보할 새로운 자료원이 필요하다. 한편 트랜스포머 아키텍처를 기반으로 사전훈련 언어모델이 등장하면서 다양한 도메인에서 자연어처리 기술 적용이 가속화되었다. 이러한 맥락에서 본 학위 논문에서는 약물 감시를 위한 다음 2가지 정보 추출 문제를 자연어처리 문제 형태로 정의하고 관련 기준 모델을 개발하였다: 1) 수동적 약물 감시 체계에 보고된 이상사례 서술자료에서 포괄적인 약물 안전성 정보를 추출한다. 2) 영문 의약학 논문 초록에서 약물-식품 상호작용 정보를 추출한다. 이를 위해 안전성 정보 추출을 위한 어노테이션 가이드라인을 개발하고 수작업으로 어노테이션을 수행하였다. 결과적으로 고품질의 자연어 학습데이터를 기반으로 사전학습 언어모델을 미세 조정함으로써 비정형 텍스트에서 임상 정보를 추출하는 강력한 자연어처리 모델 개발이 가능함을 확인하였다. 마지막으로 본 학위 논문에서는 약물감시와 관련된임상 정보 추출을 위한 어노테이션 가이드라인을 개발할 때 고려해야 할 주의 사항에 대해 논의하였다. 본 학위 논문에서 소개한 자연어 학습데이터와 자연어처리 모델은 약물 안전성 정보의 보고 품질을 향상시키고 자료원을 확장하여 약물 감시 활동을 보조할 것으로 기대된다.Chapter 1 1 1.1 Contributions of this dissertation 2 1.2 Overview of this dissertation 2 1.3 Other works 3 Chapter 2 4 2.1 Pharmacovigilance 4 2.2 Biomedical NLP for pharmacovigilance 6 2.2.1 Pre-trained language models 6 2.2.2 Corpora to extract clinical information for pharmacovigilance 9 Chapter 3 11 3.1 Motivation 12 3.2 Proposed Methods 14 3.2.1 Data source and text corpus 15 3.2.2 Annotation of ADE narratives 16 3.2.3 Quality control of annotation 17 3.2.4 Pretraining KAERS-BERT 18 3.2.6 Named entity recognition 20 3.2.7 Entity label classification and sentence extraction 21 3.2.8 Relation extraction 21 3.2.9 Model evaluation 22 3.2.10 Ablation experiment 23 3.3 Results 24 3.3.1 Annotated ICSRs 24 3.3.2 Corpus statistics 26 3.3.3 Performance of NLP models to extract drug safety information 28 3.3.4 Ablation experiment 31 3.4 Discussion 33 3.5 Conclusion 38 Chapter 4 39 4.1 Motivation 39 4.2 Proposed Methods 43 4.2.1 Data source 44 4.2.2 Annotation 45 4.2.3 Quality control of annotation 49 4.2.4 Baseline model development 49 4.3 Results 50 4.3.1 Corpus statistics 50 4.3.2 Annotation Quality 54 4.3.3 Performance of baseline models 55 4.3.4 Qualitative error analysis 56 4.4 Discussion 59 4.5 Conclusion 63 Chapter 5 64 5.1 Issues around defining a word entity 64 5.2 Issues around defining a relation between word entities 66 5.3 Issues around defining entity labels 68 5.4 Issues around selecting and preprocessing annotated documents 68 Chapter 6 71 6.1 Dissertation summary 71 6.2 Limitation and future works 72 6.2.1 Development of end-to-end information extraction models from free-texts to database based on existing structured information 72 6.2.2 Application of in-context learning framework in clinical information extraction 74 Chapter 7 76 7.1 Annotation Guideline for "Extraction of Comprehensive Drug Safety Information from Adverse Event Narratives Reported through Spontaneous Reporting System" 76 7.2 Annotation Guideline for "Extraction of Drug-Food Interactions from the Abtracts of Biomedical Articles" 100박

Digital Pharmacovigilance: the medwatcher system for monitoring adverse events through automated processing of internet social media and crowdsourcing

Thesis (Ph.D.)--Boston UniversityHalf of Americans take a prescription drug, medical devices are in broad use, and population coverage for many vaccines is over 90%. Nearly all medical products carry risk of adverse events (AEs), sometimes severe. However, pre- approval trials use small populations and exclude participants by specific criteria, making them insufficient to determine the risks of a product as used in the population. Existing post-marketing reporting systems are critical, but suffer from underreporting. Meanwhile, recent years have seen an explosion in adoption of Internet services and smartphones. MedWatcher is a new system that harnesses emerging technologies for pharmacovigilance in the general population. MedWatcher consists of two components, a text-processing module, MedWatcher Social, and a crowdsourcing module, MedWatcher Personal. With the natural language processing component, we acquire public data from the Internet, apply classification algorithms, and extract AE signals. With the crowdsourcing application, we provide software allowing consumers to submit AE reports directly. Our MedWatcher Social algorithm for identifying symptoms performs with 77% precision and 88% recall on a sample of Twitter posts. Our machine learning algorithm for identifying AE-related posts performs with 68% precision and 89% recall on a labeled Twitter corpus. For zolpidem tartrate, certolizumab pegol, and dimethyl fumarate, we compared AE profiles from Twitter with reports from the FDA spontaneous reporting system. We find some concordance (Spearman's rho= 0.85, 0.77, 0.82, respectively, for symptoms at MedDRA System Organ Class level). Where the sources differ, milder effects are overrepresented in Twitter. We also compared post-marketing profiles with trial results and found little concordance. MedWatcher Personal saw substantial user adoption, receiving 550 AE reports in a one-year period, including over 400 for one device, Essure. We categorized 400 Essure reports by symptom, compared them to 129 reports from the FDA spontaneous reporting system, and found high concordance (rho = 0.65) using MedDRA Preferred Term granularity. We also compared Essure Twitter posts with MedWatcher and FDA reports, and found rho= 0.25 and 0.31 respectively. MedWatcher represents a novel pharmacoepidemiology surveillance informatics system; our analysis is the first to compare AEs across social media, direct reporting, FDA spontaneous reports, and pre-approval trials

Prevention and control of HPV infection and HPV-related cancers in Colombia- a meeting report.

The Human Papillomavirus (HPV) Prevention and Control Board is an independent multidisciplinary board of international experts that disseminates relevant information on HPV to a broad array of stakeholders and provides guidance on strategic, technical and policy issues in the implementation of HPV control programs. In response to drastic drop of vaccine coverage following the adverse event crisis in Carmen del Bolivar, Colombia, the HPV Prevention and Control Board in collaboration with the Colombian National Cancer Institute and Colombian League Against Cancer convened a meeting in Bogota, Columbia (November 2018). The goal of the meeting was to bring together national and international group of experts to report the disease burden, epidemiology and surveillance of HPV and HPV-related cancers, to discuss the successes and especially the challenges of HPV vaccination and screening in Colombia, as well as the lessons learnt from neighbouring countries. The meeting provided a platform to confer various stakeholder's perspectives, including the role of the Colombian healthcare system and to catalyse various parts of the public health community in Colombia into effective action. The conclusion of the meeting included following suggestions to strengthen HPV prevention and control: 1) Re-introducing school-based vaccine programs, 2) Integrating primary and secondary prevention programs, 3) Developing an innovative crisis communication plan targeting healthcare workers, teachers and general population, 4) Building trust through efficient and timely communication, 5) Building strong relationship with media to ensure a stable vaccination campaign support, and 6) Promoting empathy among healthcare professionals towards patients to build trust and communicate effectively