Estimation of the prevalence of adverse drug reactions from social media

The Value of Social Media Analysis for Adverse Events Detection and Pharmacovigilance: Scoping Review

BACKGROUND

Adverse drug events pose an enormous public health burden, leading to hospitalization, disability, and death. Even the adverse events (AEs) categorized as nonserious can severely impact on patient's quality of life, adherence, and persistence. Monitoring medication safety is challenging. Web-based patient reports on social media may be a useful supplementary source of real-world data. Despite the growth of sophisticated techniques for identifying AEs using social media data, a consensus has not been reached as to the value of social media in relation to more traditional data sources.

OBJECTIVE

This study aims to evaluate and characterize the utility of social media analysis in adverse drug event detection and pharmacovigilance as compared with other data sources (such as spontaneous reporting systems and the clinical literature).

METHODS

In this scoping review, we searched 11 bibliographical databases and Google Scholar, followed by handsearching and forward and backward citation searching. Each record was screened by 2 independent reviewers at both the title and abstract stage and the full-text screening stage. Studies were included if they used any type of social media (such as Twitter or patient forums) to detect AEs associated with any drug medication and compared the results ascertained from social media to any other data source. Study information was collated using a piloted data extraction sheet. Data were extracted on the AEs and drugs searched for and included; the methods used (such as machine learning); social media data source; volume of data analyzed; limitations of the methodology; availability of data and code; comparison data source and comparison methods; results, including the volume of AEs, and how the AEs found compared with other data sources in their seriousness, frequencies, and expectedness or novelty (new vs known knowledge); and conclusions.

RESULTS

Of the 6538 unique records screened, 73 publications representing 60 studies with a wide variety of extraction methods met our inclusion criteria. The most common social media platforms used were Twitter and online health forums. The most common comparator data source was spontaneous reporting systems, although other comparisons were also made, such as with scientific literature and product labels. Although similar patterns of AE reporting tended to be identified, the frequencies were lower in social media. Social media data were found to be useful in identifying new or unexpected AEs and in identifying AEs in a timelier manner.

CONCLUSIONS

There is a large body of research comparing AEs from social media to other sources. Most studies advocate the use of social media as an adjunct to traditional data sources. Some studies also indicate the value of social media in understanding patient perspectives such as the impact of AEs, which could be better explored.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/47068.

Which Social Media Platforms Provide the Most Informative Data for Monitoring the Opioid Crisis?

Background and Aims

Social media can provide real-time insight into trends in substance use, addiction, and recovery. Prior studies have leveraged data from platforms such as Reddit and X (formerly Twitter), but evolving policies around data access have threatened their usability in opioid overdose surveillance systems. Here, we evaluate the potential of a broad set of platforms to detect emerging trends in the opioid crisis.

Design

We identified 72 online platforms with a substantial global user base or prior citations in opioid-related research. We evaluated each platform's fit with our definition of social media, size of North American user base, and volume of opioid-related discourse. We created a shortlist of 11 platforms that met our criteria. We documented basic characteristics, volume and nature of opioid discussion, official policies regulating drug-related discussion, and data accessibility of shortlisted platforms.

Setting

USA and Canada.

Measurements

We quantified the volume of opioid discussion by number of platform-specific Google search hits for opioid terms. We captured informal language by including slang generated using a large language model. We report the number of opioid-related hits and proportion of opioid-related hits to hits for common nouns.

Findings

We found that TikTok, YouTube, and Facebook have the most potential for use in opioid-related surveillance. TikTok and Facebook have the highest relative amount of drug-related discussions. Language on TikTok was predominantly informal. Many platforms offer data access tools for research, but changing company policies and user norms create instability. The demographics of users varies substantially across platforms.

Conclusions

Social media data sources hold promise for detecting trends in opioid use, but researchers must consider the utility, accessibility, and stability of data on each platform. A strategy mixing several platforms may be required to cover all demographics suffering in the epidemic.

Human Endocrine-Disrupting Effects of Phthalate Esters through Adverse Outcome Pathways: A Comprehensive Mechanism Analysis

Phthalate esters (PAEs) are widely exposed in the environment as plasticizers in plastics, and they have been found to cause significant environmental and health hazards, especially in terms of endocrine disruption in humans. In order to investigate the processes underlying the endocrine disruption effects of PAEs, three machine learning techniques were used in this study to build an adverse outcome pathway (AOP) for those effects on people. According to the results of the three machine learning techniques, the random forest and XGBoost models performed well in terms of prediction. Subsequently, sensitivity analysis was conducted to identify the initial events, key events, and key features influencing the endocrine disruption effects of PAEs on humans. Key features, such as Mol.Wt, Q+, QH+, ELUMO, minHCsats, MEDC-33, and EG, were found to be closely related to the molecular structure. Therefore, a 3D-QSAR model for PAEs was constructed, and, based on the three-dimensional potential energy surface information, it was discovered that the hydrophobic, steric, and electrostatic fields of PAEs significantly influence their endocrine disruption effects on humans. Lastly, an analysis of the contributions of amino acid residues and binding energy (BE) was performed, identifying and confirming that hydrogen bonding, hydrophobic interactions, and van der Waals forces are important factors affecting the AOP of PAEs' molecular endocrine disruption effects. This study defined and constructed a comprehensive AOP for the endocrine disruption effects of PAEs on humans and developed a method based on theoretical simulation to characterize the AOP, providing theoretical guidance for studying the mechanisms of toxicity caused by other pollutants.

Engaging Patients via Online Healthcare Fora: Three Pharmacovigilance Use Cases

Increasingly, patient-generated safety insights are shared online, via general social media platforms or dedicated healthcare fora which give patients the opportunity to discuss their disease and treatment options. We evaluated three areas of potential interest for the use of social media in pharmacovigilance. To evaluate how social media may complement existing safety signal detection capabilities, we identified two use cases (drug/adverse event [AE] pairs) and then evaluated the frequency of AE discussions across a range of social media channels. Changes in frequency over time were noted in social media, then compared to frequency changes in Food and Drug Administration Adverse Event Reporting System (FAERS) data over the same time period using a traditional disproportionality method. Although both data sources showed increasing frequencies of AE discussions over time, the increase in frequency was greater in the FAERS data as compared to social media. To demonstrate the robustness of medical/AE insights of linked posts we manually reviewed 2,817 threads containing 21,313 individual posts from 3,601 unique authors. Posts from the same authors were linked together. We used a quality scoring algorithm to determine the groups of linked posts with the highest quality and manually evaluated the top 16 groups of posts. Most linked posts (12/16; 75%) contained all seven relevant medical insights assessed compared to only one (of 1,672) individual post. To test the capability of actively engage patients via social media to obtain follow-up AE information we identified and sent consents for follow-up to 39 individuals (through a third party). We sent target follow-up questions (identified by pharmacovigilance experts as critical for causality assessment) to those who consented. The number of people consenting to follow-up was low (20%), but receipt of follow-up was high (75%). We observed completeness of responses (37 out of 37 questions answered) and short average time required to receive the follow-up (1.8 days). Our findings indicate a limited use of social media data for safety signal detection. However, our research highlights two areas of potential value to pharmacovigilance: obtaining more complete medical/AE insights via longitudinal post linking and actively obtaining rapid follow-up information on AEs.

Determining the prevalence of cannabis, tobacco, and vaping device mentions in online communities using natural language processing

INTRODUCTION

The relationship between cannabis, tobacco, and vaping devices is both rapidly changing and poorly understood, with consumers rapidly shifting between use of all three product types. Given this dynamic and evolving landscape, there is an urgent need to monitor and better understand co-use, dual-use, and transition patterns between these products. This study describes work that utilizes social media - in this case, Reddit - in conjunction with automated Natural Language Processing (NLP) methods to better understand cannabis, tobacco, and vaping device product usage patterns.

METHODS

We collected Reddit data from the period 2013-2018, sourced from eight popular, high-volume Reddit communities (subreddits) related to the three product categories. We then manually annotated (coded) a set of 2640 Reddit posts and trained a machine learning-based NLP algorithm to automatically identify and disambiguate between cannabis or tobacco mentions (both smoking and vaping) in Reddit posts. This classifier was then applied to all data derived from the eight subreddits, 767,788 posts in total.

RESULTS

The NLP algorithm achieved an overall moderate performance (overall F-score of 0.77). When applied to our large corpus of Reddit posts, we discovered that over 10% of posts in the smoking cessation subreddit r/stopsmoking were classified as referring to vaping nicotine, and that only 2% of posts from the subreddits r/electronic_cigarette and r/vaping were classified as referring to smoking (tobacco) cessation.

CONCLUSIONS

This study presents the results of applying an NLP algorithm designed to identify and distinguish between cannabis and tobacco mentions (both smoking and vaping) in Reddit posts, hence contributing to our currently limited understanding of co-use, dual-use, and transition patterns between these products.

Using a mixed methods approach to identify public perception of vaping risks and overall health outcomes on Twitter during the 2019 EVALI outbreak

INTRODUCTION

Vaping product use (i.e., e-cigarettes) has been rising since 2000 in the United States. Negative health outcomes associated with vaping products have created public uncertainty and debates on social media platforms. This study explores the feasibility of using social media as a surveillance tool to identify relevant posts and at-risk vaping users.

METHODS

Using an interdisciplinary method that leverages natural language processing and manual content analysis, we extracted and analyzed 794,620 vaping-related tweets on Twitter. After observing significant increases in vaping-related tweets in July, August, and September 2019, additional human coding was completed on a subset of these tweets to better understand primary themes of vaping-related discussions on Twitter during this time frame.

RESULTS

We found significant increases in tweets related to negative health outcomes such as acute lung injury and respiratory issues during the outbreak of e-cigarette/vaping associated lung injury (EVALI) in the fall of 2019. Positive sentiment toward vaping remained high, even across the peak of this outbreak in July, August, and September. Tweets mentioning the public perceptions of youth risk were concerning, as were increases in marketing and marijuana-related tweets during this time.

DISCUSSION

The preliminary results of this study suggest the feasibility of using Twitter as a means of surveillance for public health crises, and themes found in this research could aid in specifying those groups or populations at risk on Twitter. As such, we plan to build automatic detection algorithms to identify these unique vaping users to connect them with a digital intervention in the future.

A semantic similarity based methodology for predicting protein-protein interactions: Evaluation with P53-interacting kinases

Biomedical literature contains unstructured, rich information regarding proteins, ligands, diseases as well as biological pathways in which they are involved. Systematically analyzing such textual corpus has the potential for biomedical discovery of new protein-protein interactions and hidden drug indications. For this purpose, we have investigated a methodology that is based on a well-established text mining tool, Word2Vec, for the analysis of PubMed full text articles to derive word embeddings, and the use of a simple semantic similarity comparison either by itself or in conjunction with k-Nearest Neighbor (kNN) technique for the prediction of new relationships. To test this methodology, three lines of retrospective analyses of a dataset with known P53-interacting proteins have been conducted. First, we demonstrated that Word2Vec semantic similarity can infer functional relatedness among all kinases known to interact with P53. Second, in a series of time-split experiments, we demonstrated that both a simple similarity comparison and kNN models built with papers published up to a certain year were able to discover P53 interactors described in later publications. Third, in a different scenario of time-split experiments, we examined the predictions of P53-interacting proteins based on the kNN models built on data prior to a certain split year for different time ranges past that year, and found that the cumulative number of correct predictions was indeed increasing with time. We conclude that text mining of research papers in the PubMed literature based on Word2Vec analysis followed by a simple similarity comparison or kNN modeling affords excellent predictions of protein-protein interactions between P53 and kinases, and should have wide applications in translational biomedical studies such as repurposing of existing drugs, drug-drug interaction, and elucidation of mechanisms of action for drugs.

A Thematic Network-Based Methodology for the Research Trend Identification in Building Energy Management

The rapid increase in the number of online resources and academic articles has created great challenges for researchers and practitioners to efficiently grasp the status quo of building energy-related research. Rather than relying on manual inspections, advanced data analytics (such as text mining) can be used to enhance the efficiency and effectiveness in literature reviews. This article proposes a text mining-based approach for the automatic identification of major research trends in the field of building energy management. In total, 5712 articles (from 1972 to 2019) are analyzed. The word2vec model is used to optimize the latent Dirichlet allocation (LDA) results, and social networks are adopted to visualize the inter-topic relationships. The results are presented using the Gephi visualization platform. Based on inter-topic relevance and topic evolutions, in-depth analysis has been conducted to reveal research trends and hot topics in the field of building energy management. The research results indicate that heating, ventilation, and air conditioning (HVAC) is one of the most essential topics. The thermal environment, indoor illumination, and residential building occupant behaviors are important factors affecting building energy consumption. In addition, building energy-saving renovations, green buildings, and intelligent buildings are research hotspots, and potential future directions. The method developed in this article serves as an effective alternative for researchers and practitioners to extract useful insights from massive text data. It provides a prototype for the automatic identification of research trends based on text mining techniques.

Mining Social Media Data for Biomedical Signals and Health-Related Behavior

Social media data have been increasingly used to study biomedical and health-related phenomena. From cohort-level discussions of a condition to population-level analyses of sentiment, social media have provided scientists with unprecedented amounts of data to study human behavior associated with a variety of health conditions and medical treatments. Here we review recent work in mining social media for biomedical, epidemiological, and social phenomena information relevant to the multilevel complexity of human health. We pay particular attention to topics where social media data analysis has shown the most progress, including pharmacovigilance and sentiment analysis, especially for mental health. We also discuss a variety of innovative uses of social media data for health-related applications as well as important limitations of social media data access and use.

"Chasing the first high": memory sampling in drug choice

Although vivid memories of drug experiences are prevalent within clinical contexts and addiction folklore ("chasing the first high"), little is known about the relevance of cognitive processes governing memory retrieval to substance use disorder. Drawing on recent work that identifies episodic memory's influence on decisions for reward, we propose a framework in which drug choices are biased by selective sampling of individual memories during two phases of addiction: (i) downward spiral into persistent use and (ii) relapse. Consideration of how memory retrieval influences the addiction process suggests novel treatment strategies. Rather than try to break learned associations between drug cues and drug rewards, treatment should aim to strengthen existing and/or create new associations between drug cues and drug-inconsistent rewards.

Mining HPV Vaccine Knowledge Structures of Young Adults From Reddit Using Distributional Semantics and Pathfinder Networks

The human papillomavirus (HPV) vaccine protects adolescents and young adults from 9 high-risk HPV virus types that cause 90% of cervical and anal cancers and 70% of oropharyngeal cancers. This study extends our previous research analyzing online content concerning the HPV vaccination in social media platforms used by young adults, in which we used Pathfinder network scaling and methods of distributional semantics to characterize differences in knowledge organization reflected in consumer- and expert-generated online content. The current study extends this approach to evaluate HPV vaccine perceptions among young adults who populate Reddit, a major social media platform. We derived Pathfinder networks from estimates of semantic relatedness obtained by learning word embeddings from Reddit posts and compared these to networks derived from human expert estimation of the relationship between key concepts. Results revealed that users of Reddit, predominantly comprising young adults in the vaccine catch up age-group 18 through 26 years of age, perceived the HPV vaccine domain from a virus-framed perspective that could impact their lifestyle choices and that their awareness of the HPV vaccine for cancer prevention is also lacking. Further differences in knowledge structures were elucidated, with implications for future health communication initiatives.

Using word embeddings to expand terminology of dietary supplements on clinical notes

Objective

The objective of this study is to demonstrate the feasibility of applying word embeddings to expand the terminology of dietary supplements (DS) using over 26 million clinical notes.

Methods

Word embedding models (ie, word2vec and GloVe) trained on clinical notes were used to predefine a list of top 40 semantically related terms for each of 14 commonly used DS. Each list was further evaluated by experts to generate semantically similar terms. We investigated the effect of corpus size and other settings (ie, vector size and window size) as well as the 2 word embedding models on performance for DS term expansion. We compared the number of clinical notes (and patients they represent) that were retrieved using the word embedding expanded terms to both the baseline terms and external DS sources expanded terms.

Results

Using the word embedding models trained on clinical notes, we could identify 1–12 semantically similar terms for each DS. Using the word embedding expanded terms, we were able to retrieve averagely 8.39% more clinical notes and 11.68% more patients for each DS compared with 2 sets of terms. The increasing corpus size results in more misspellings, but not more semantic variants and brand names. Word2vec model is also found more capable of detecting semantically similar terms than GloVe.

Conclusion

Our study demonstrates the utility of word embeddings on clinical notes for terminology expansion on 14 DS. We propose that this method can be potentially applied to create a DS vocabulary for downstream applications, such as information extraction.

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.

Artificial Intelligence for Participatory Health: Applications, Impact, and Future Implications

Objective : Artificial intelligence (AI) provides people and professionals working in the field of participatory health informatics an opportunity to derive robust insights from a variety of online sources. The objective of this paper is to identify current state of the art and application areas of AI in the context of participatory health.

Methods : A search was conducted across seven databases (PubMed, Embase, CINAHL, PsychInfo, ACM Digital Library, IEEExplore, and SCOPUS), covering articles published since 2013. Additionally, clinical trials involving AI in participatory health contexts registered at clinicaltrials.gov were collected and analyzed.

Results : Twenty-two articles and 12 trials were selected for review. The most common application of AI in participatory health was the secondary analysis of social media data: self-reported data including patient experiences with healthcare facilities, reports of adverse drug reactions, safety and efficacy concerns about over-the-counter medications, and other perspectives on medications. Other application areas included determining which online forum threads required moderator assistance, identifying users who were likely to drop out from a forum, extracting terms used in an online forum to learn its vocabulary, highlighting contextual information that is missing from online questions and answers, and paraphrasing technical medical terms for consumers.

Conclusions : While AI for supporting participatory health is still in its infancy, there are a number of important research priorities that should be considered for the advancement of the field. Further research evaluating the impact of AI in participatory health informatics on the psychosocial wellbeing of individuals would help in facilitating the wider acceptance of AI into the healthcare ecosystem.

Examining the effect of prescription sequence on developing adverse drug reactions: The case of renal failure in diabetic patients

OBJECTIVES

While the effect of medications in development of Adverse Drug Reactions (ADRs) have been widely studied in the past, the literature lacks sufficient coverage in investigating whether the sequence in which [ADR-prone] drugs are prescribed (and administered) can increase the chances of ADR development. The present study investigates this potential effect by applying emergent sequential pattern mining techniques to electronic health records.

MATERIALS AND METHODS

Using longitudinal medication and diagnosis records from more than 377,000 diabetic patients, in this study, we assessed the possible effect of prescription sequences in developing acute renal failure as a prevalent ADR among this group of patients. Relying on emergent sequential pattern mining, two statistical case-control approaches were designed and employed for this purpose.

RESULTS

The results taken from the two employed approaches (i.e. 76.7% total agreement and 68.4% agreement on the existence of some significant effect) provide evidence for the potential effect of prescription sequence on ADRs development evidenced by the discovery that certain sequential patterns occurred more frequently in one group of patients than the other.

CONCLUSION

Given the significant effects shown by our data analyses, we believe that design and implementation of automated clinical decision support systems to constantly monitor patients' medication transactions (and the sequence in which they are administered) and make appropriate alerts to prevent certain possible ADRs, may decrease ADR occurrences and save lives and money.

The value of unsolicited online data in drug policy research

We alert readers to the value of using unsolicited online data in drug policy research by highlighting web-based content relevant to drug policy generated by distinct types of actor: people who consume, supply or produce illicit drugs, online news websites and state or civil society organisations. These actors leave 'digital traces' across a range of internet platforms, and these traces become available to researchers to use as data - although they have not been solicited by researchers, and so have not been created specifically to fulfil the aims of research projects. This particular type of data entails certain strengths, limitations and ethical challenges, and we aim to assist researchers in understanding these by drawing on selected examples of published research using unsolicited online data that have generated valuable drug policy insights not possible using other traditional data sources. We argue for the continued and increased importance of using unsolicited online data so that drug policy scholarship keep pace with recent developments in the global landscape of drug policies and illicit drug practices.

HARNESSING SOCIAL MEDIA FOR HEALTH INFORMATION MANAGEMENT

The remarkable upsurge of social media has dramatic impacts on health care research and practice in the past decade. Social media are reshaping health information management in a variety of ways, ranging from providing cost-effective ways to improve clinician-patient communication and exchange health-related information and experience, to enabling the discovery of new medical knowledge and information. Despite some demonstrated initial success, social media use and analytics for improving health as a research field is still at its infancy. Information systems researchers can potentially play a key role in advancing the field. This study proposes a conceptual framework for social media-based health information management by drawing on multi-disciplinary research. With the guidance of the framework, this research presents related research challenges, identifies important yet under-explored research issues, and discusses promising directions for future research.