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Pharmacovigilance Analytics

How Analytics Are Transforming Pharmacovigilance

Within the last decade, there has been a growing awareness that the scope of Pharmacovigilance (PV) should be extended beyond the strict framework of detecting signals of safety concern. Nowadays, PV organizations face increasing pressures to enhance their analytic capabilities and become a value-added partner through the product development lifecycle. Increased regulatory scrutiny and greater emphasis on safety from consumers provide added pressure for companies to ensure that they are being proactive about accurately monitoring and assessing the benefit-risk profile of a medicinal product as early as possible in the product’s lifecycle. This is where pharmacovigilance analytics come into place.

Pharmacovigilance Analytics Defined

Pharmacovigilance analytics can be defined as the use of advanced analytic techniques with the purpose of examining large and varied data sets containing safety information, to uncover hidden patterns, unknown correlations, trends, patient preferences and other useful information that can help organizations make more-informed business decisions.

The effective management of safety data across multiple platforms is critical for the analysis and understanding of safety events. In an increasingly challenging business environment, pharmacovigilance analytics provides an opportunity to better utilize data, both to comply with regulatory authorities reporting requirements, and to drive actionable insights that can predict and prevent adverse events (AE).

Modern companies will use a value-based approach for pharmacovigilance analytics. This type of approach emphasizes quality and prevention over other aspects of the classical PV work. While the classical approach to AE analysis responds to Reporting (What happened?) and basic Monitoring (What is happening now?), we need to go beyond that, by enhancing our monitoring activities and being able to cover and respond to additional aspects like Evaluation (Why did it happen?), Prediction (What will happen?), and Prescription (To whom will it happen?).

Purpose of Pharmacovigilance Analytics

Our purpose should be to establish a PV data analytics process designed to leverage big data and the benefits of using such data across the value chain to build synergy between traditional (including regulatory obligations) analytics and big data analytics to provide faster and better insights to the organization.

Pharmacovigilance analytics serves as one of the instruments for the continuous monitoring of pharmacovigilance data. All available evidence on the benefit-risk balance of medicinal products and all their relevant aspects should be sought. All new information that could have an impact on the benefit-risk balance and the use of a product, should be considered for decision making.

Basically, in the framework of pharmacovigilance, PV analytics should be applied to gain insights by integrating data related to medicinal products from multiple sources and applying techniques to search, compare, and summarize them.

Overview of Pharmacovigilance Analytics

Pharmacovigilance departments must have in place the ability to quickly identify risks based on internal and external information, through processes that identify and extract product and indication-specific information from across the organization.

PV analytics will be used for, but not limited to:

  • Monitoring of compliance regarding AE / case management
  • Supporting analysis for signal detection
  • Contributing to the elaboration of benefit-risk assessments (as stand-alone, or as part of regulatory aggregate reports), and
  • Providing knowledge discovery on the factors governing the association between the exposure to a medicinal product and its effects on the population

The company will be able to leverage the knowledge discovery process and benefit of its results across the organization. For example, new insights can be used for the drug discovery process, and to prevent reputation and monetary loss from withdrawal of the medicinal product.

PV analytics uses data integration. The analysis of data integrated from multiple sources provides a synergy that generates real value, in contrast to multiple-step analyses that make it difficult to understand the big picture.

For that purpose, PV analytics applies new techniques for analysis of data including, but not limited to, data mining, text and information mining, and visualization tools. For more detailed information about analytical methods and tools click here.

Following we develop the main aspects of PV analytics enumerated in the Overview section.

Adverse Event / Case Management Compliance Monitoring

The biggest challenges facing pharmacovigilance are the rising and unpredictable AE case volumes, increasing complexity and cost, a lack of investment in new technologies (automation) and process improvements, as well as shortage of well-defined metrics. All these challenges can contribute to a general decrease in operational performance and ultimately case quality or compliance. To avoid and prevent these problems, companies are encouraged to set up an AE / Case Management compliance monitoring system.

Monitoring can be done by PV scientists, in collaboration with PV operations. They will monitor PV organization’s operational efficiency, including case processing, identification of issues in case workflow, contract research organization (CRO) management, and case processor management.

Proposed metrics for this section are:

  • AE analytics
  • Case processing metrics
  • Case submission metrics
  • Key performance indicators
  • Trend analysis

Data sources: Safety Database

Signal Detection and Management Analytics

In accordance with processes governing Signal Detection and Management, PV scientist generates a monthly report for each company product. The report is delivered not later than 7 business days following the reporting month, and includes the following metrics for the reporting month, including cumulative statistics:

Proposed metrics, applicable to all AEs, highlighting Designated Medical Events (DME), and Targeted Medical Events (TME):

  • Descriptive analysis of AEs by expectedness, causality, severity and outcome
  • Reporting rates of AEs submitted, by geographic area, age, gender, and race, classified using MedDRA Preferred Term (PT) and System Organ Class (SOC)
  • Proportional reporting ratio (PRR), and reporting odds ratio (ROR), including their statistical significance, calculated for each AE on a monthly basis and cumulative
  • Trend analysis of the previous metrics

Data sources: a combination of safety database, FDA Adverse Event Reporting System (FAERS), EMA EudraVigilance, and WHO Vigibase

Active Benefit-Risk Identification and Analysis

Inspired in ARIA (Active Risk Identification and Analysis) model from FDA Sentinel Initiative. PV analytics will use an integrated, active benefit-risk identification and analysis system. This system will be comprised of pre-defined, parametrized, , and re-usable querying tools that will enable safety surveillance using company data platform, including medical operations, as well as commercial operations databases.

The objective of this part of the PV analytics operation is to take advantage of the enormous amount of healthcare data that is generated on daily basis. By using up-to-date analytic methods, PV analytics will be able to promptly identify emerging risks (and possibly benefits), as well as to acquire better insights on the safety profile of company medicinal products.

Pattern identification of product-event combinations, multivariable classification of risks, and the identification of factors associated with the risk of experiencing an AE, are among the main objectives of this section. This will allow the creation of models that are able to estimate the probability of an AE for a given group of patients, with the ultimate goal of utilizing this information for the prevention of such AEs.

Specifically, this section wants to provide analytical support for the benefit-risk assessment of medicinal products, being ad-hoc or to be added to regulatory safety reports requiring benefit-risk analysis.

PV analytics will create algorithms for use in administrative and clinical data environments to identify company-prioritized health outcomes that may be related to company medicinal products. Apart from all potential AEs company designated medical events and targeted medical events will be specifically monitored.

Queries will be created for, but not limited to:

  • Calculation of event rates of exposure, outcomes and conditions
  • Identification of the exposure of interest (company medicinal product, same-class products), and determination of the exposed time
  • Identification of most frequently observed event codes
  • Identification of the exposure and treatment patterns of the company medicinal products
  • Characterization of concomitant medications
  • Estimation of propensity scores following the identification of exposures, follow-up times, exposures and covariates
  • Estimation of treatment effects, including hazard ratios and incidence rate differences

Active surveillance using sequential monitoring

  • Given the longitudinal nature of the AE monitoring system, a specific type of statistical tools is required. One approach applied to new safety systems using electronic data to assess safety is sequential monitoring, which permits repeated estimation and testing of associations between a new medicinal product and potential AEs over time.
  • Sequential analysis computes the test statistic at periodic time intervals as data accumulate, compares this test statistic to a prespecified signaling threshold, and stops if the observed test statistic is more extreme than the threshold. This way, sequential test can facilitate earlier identification of safety signals as soon as sufficient information from the electronic health care database becomes available to detect elevated AE risks.
  • Although used extensively in clinical development, the application of sequential analysis to postmarket surveillance is relatively new. The following planning steps will be applied to safety evaluations in observational, electronic health-care database settings, either for a one-time analysis or multiple sequential analyses over time:
    • Use available data (or existing literature) to conduct a feasibility assessment and prespecify the surveillance plan. Pre-specification of the surveillance design and analytical plan is critical.
    • Describe uptake for the product of interest to determine if we will have enough sample size for the analysis. Use existing data to inform surveillance planning can reduce the number of assumptions that need to be made at the planning phase and, in turn, minimize downstream changes to initial sequential plans.
    • Statistically evaluate, jointly select, and clearly communicate the final sequential design. Selection of a sequential design should include statistical evaluation and clear communication of the sequential design and analysis with all those designing and interpreting the safety surveillance activity so that the operating characteristics are well understood in advance of implementation.
  • Finally, reports will be generated to reflect knowledge acquired on benefits and risks that appeared during the time window covered by the report. Ad-hoc reports will be created when needed.

Disproportional Recording vs Disproportional Reporting

April 1, 2018 by Jose Rossello Leave a Comment

Signals of Disproportional Recording – Seriously?

I have read with great interest a paper recently published in Drug Safety journal (Zhou X, Douglas IJ, Shen R, Bate A. Signal Detection for Recently Approved Products: Adapting and Evaluating Self-Controlled Case Series Method Using a US Claims and UK Electronic Medical Records Database. Drug Saf. https://doi.org/10.1007/s40264-017-0626-y). As always, I read with attention all papers authored by Andrew Bate and his team.

Something in the paper that called my attention was the concept of “Signals of Disproportional Recording (SDRs)“. I read it first in the abstract, and my first thought was that it was an error, and the authors were actually referring to Signals of Disproportionate Reporting (SDRs). Signals of disproportionate reporting are understood as statistical associations between medicinal products and adverse events i.e. drug-event pairs. When a SDR is identified for a medicinal product, this adverse event is reported relatively more frequently in association with this medicinal product than with other medicinal products (Practical Aspects of Signal Detection in Pharmacovigilance : Report of CIOMS Working Group VIII. CIOMS, Geneva, 2010).

Of course, it was not a mistake. The Analysis Methods section of the paper explains it clearly:

Incidence rate ratios (IRRs) are calculated by comparing the rate of events in a given post-exposure period (risk period) with the rate of events in unexposed periods absent of the exposure (all other observed times). In a signal screening framework, statistical uncertainty is examined based on the 95% confidence interval (CI) of the IRR estimates. Specifically, when the lower bound of the 95% CI of the IRR estimate is > 1, this is considered a positive finding and is a Signal of Disproportional Recording (SDR) analogous to SDRs in spontaneous reporting, which are findings of potential interest that have not undergone clinical review to be considered signals of suspected causality.

But I wanted to know more about this new concept. Who was the first one to use it on a scientific communication? I did a little research and found one reference for the same concept: A. Bate. Tuning Epidemiological Study Design Methods for Exploratory Data Analysis in Real World Data. Abstract presented in 15th ISOP Annual Meeting “Cubism in Pharmacovigilance” Prague, Czech Republic 27-30 October, 2015.

So, it is clear that the concept of signal of disproportional recording has been first used by Andrew Bate. The concept is brilliant. The finding of an adverse event-drug pair in an electronic health record happens because someone recorded it, independently of whether or not is was also reported.

Even though secondary use of electronic healthcare records (EHR) and insurance claims data for hypothesis testing has occurred for many decades, signal detection activities to identify potential drug safety issues has historically focused primarily on spontaneous reports. But there is an increasing interest on using EHR for signal detection in pharmacovigilance. Electronic health records exhibit special characteristics (longitudinal nature, partially unstructured data) that is requiring to adapt old analytics to this new framework, and even create new methods and concepts (Zorych I, Madigan D, Ryan P, Bates A. Disproportionality methods for pharmacovigilance in longitudinal observational databases. Stat Methods Med Res 2013; 22(1):39-56). That is, the analytic techniques used for the analysis of drug-event (or drug-outcome) pairs in spontaneous reporting of adverse events, are not directly applicable to EHR and claims data. Creative thinking and new research are welcome in this area.

The paper is interesting, not only for using “disproportional recording”, which is anecdotal. It explores how to adapt and evaluate the self-controlled case series method and its use in claims and electronic medical records databases, for the challenging aspect of signal detection in the framework of  recently approved products. You will find an interesting discussion on the appropriate risk period selection method, which may be different in drug safety signal detection, than in formal hypothesis-testing studies.

The authors conclude that self-controlled case series method may be useful for safety signal detection in EHRs, and that early identification of previously unknown safety signals may be possible shortly after a new product is launched. Performance of this method varies by the nature of both exposure and event pair and their anticipated association.

 

Filed Under: Signal Detection Tagged With: disproportional recording, disproportional reporting, SDR, signal detection

Deep Learning, Machine Learning, and Artificial Intelligence – What are the Differences?

March 18, 2018 by Jose Rossello Leave a Comment

In this video, Bernardo F. Nunes explains how these 3 concepts (artificial intelligence, machine learning, and deep learning) do not represent the same thing:

Bernardo is breaking down the 3 concepts for us, in a very easy and understandable way.

Artificial intelligence exists when a machine has cognitive capabilities, such as problem solving and learning. It’s normally associated to a human benchmark, as, for example reasoning, speech, and vision.

In the video, he differentiates 3 levels of A.I.:

  1. Narrow A.I., when a machine is better than us in a specific task (we are here now)
  2. General A.I., when a machine is like us in any intellectual task
  3. Strong A.I, when a machine is better than us in many tasks

One of the favorites early developments in A.I. is the perceptron (1957). It was a single layer of artificial neural networks designed for image recognition. They are called neural networks because the first practitioners on A.I. thought that these interconnected nodes looked like the human neural system, which has neural networks. These are the natural neural networks. The perceptron is a rudimentary version of an artificial neural network.

Machine learning, appeared on the 1980s, when a body of researchers worked on what is called supervised learning. Algorithms are trained with datasets based on past examples, in a model in which the trained algorithm is applied to a new dataset for classification purposes. Widely used for business purposes.

Deep learning makes use of deep neural networks. Shallow neural nets have only one hidden layer between the input and the output. However, deep neural nets have 2 or more hidden layers between the input and the output. It’s the responsible for the advancement in image recognition. If you can represent an image numerically, then you can process it with deep learning.

Bernardo’s conclusion is that deep learning, machine learning, and artificial intelligence are not 3 different things. They simply are subsamples of each other: deep learning belongs to machine learning, and machine learning belongs to artificial intelligence.

Filed Under: PV Analytics Tagged With: AI, artificial intelligence, deep learning, machine learning

How Organizations Use Social Media For Pharmacovigilance

March 7, 2018 by Jose Rossello 2 Comments

Pharmacovigilance and Social Media – are you there yet?

In this lecture, Alexandra Hoegberg from the Uppsala Monitoring Centre gives an interesting lecture about the role of social media in public health, particularly in pharmacovigilance. After listening to the lecture, I would have better used ‘drug safety’ instead of ‘pharmacovigilance’ in the title.

Social media channels are becoming an effective communication tool which can be used to expand reach, foster engagement, and increase access to credible, science-based health messages.

Bottom line is that it informs the public and has the potential to empower people to make safer health decisions

OK, that’s unidirectional, from institutions and official bodies to individuals / patients. But how about from individuals to institutions? In pharmacovigilance and drug safety we are interested in both sides of the equation.

The lecturer provides examples of how MHRA uses social media to reach and provide information to the population on side effects of certain drugs.

She makes good points on potential pitfalls of the use of social media from health organizations, and the possibility of having to deal with fake news that spread virally.

As an example of fake news, the lecturer mentions the case published on The Independent (7 Jan 2017):

Revealed: How Dangerous Fake Health News Conquered Facebook:

The widespread circulation of fake health news on social networks is misleading and potentially dangerous, health officials have warned.

Misinformation published by conspiracy sites about serious health conditions is often shared more widely than evidence-based reports from reputable news organisations, according to analysis by The Independent. Of the 20 most-shared articles on Facebook in 2016 with the word “cancer” in the headline, more than half report claims discredited by doctors and health authorities or – in the case of the year’s top story – directly by the source cited in the article.

Facebook has introduced measures allowing users to flag disputed news shared on the site following concerns the circulation of deliberately fictitious articles could have influenced the US election.

Public Health England and the head of the Royal College of GPs have expressed concern over the amount of made-up health news shared online, with Cancer Research UK calling for “vital” action from the social network.

Organizations must set a social media strategy and think about:

  • the purpose of their presence on social media
  • the differences among social media platforms, which ones are more used in your country, and set the tone accordingly
  • which audiences are more likely to use the platform

The lecture finalizes giving valuable tips on what organizations have to do in order to succeed in social media.

This is a good presentation on how institutions and organizations are going to use or should utilize social media. Individual companies in the pharma industry can benefit from this approach too.

Filed Under: Social Media

Twitter, Safety and Pharmacovigilance: All Papers Retrieved using PubMed

March 2, 2018 by Jose Rossello Leave a Comment

Researchers are increasingly using Twitter to analyze what people are talking about at a given time point and over time. Among the multiple uses analysis of tweets can have, safety surveillance, signal detection and discovery of adverse drug events or adverse drug reactions is something that we are just starting to explore for pharmacovigilance analytics, in the framework of social media analysis.

In this post, we are going to analyze all papers retrieved from PubMed with the search string: twitter AND (safety OR pharmacovigilance). On 02 March 2018, that search resulted in 79 search results. From them, we have hand-picked those related to the use of Twitter for drug safety / pharmacovigilance surveillance purposes. Of course there are other keyword combinations that will provide different results as, for example, “social media data/mining/monitoring”,”adverse drug reaction”, “adverse event”, and others. These other results will be covered by other posts in this series.

This is our selection in chronological order:

Bian J, Topaloglu U, Yu F. Towards Large-scale Twitter Mining for Drug-related Adverse Events. SHB12 2012;25-32.

The increasing popularity of social media platforms like Twitter presents a new information source for finding potential adverse events. Given the high frequency of user updates, mining Twitter messages can lead us to real-time pharmacovigilance.  In this paper, the authors describe an approach to find drug users and potential adverse events by analyzing the content of twitter messages utilizing Natural Language Processing (NLP) and to build Support Vector Machine (SVM) classifiers. Due to the size nature of the dataset (i.e., 2 billion Tweets), the experiments were conducted on a High Performance Computing (HPC) platform using MapReduce, which exhibits the trend of big data analytics. The results suggest that daily-life social networking data could help early detection of important patient safety issues.

Chary M, Genes N, McKenzie A, Manini AF. Leveraging Social Networks for Toxicovigilance. J Med Toxicol 2013;9(2):184-91.

The authors talk about the changing landscape of drug abuse, and that traditional means of characterizing the change are not sufficient any more, because they can miss changes in usage patterns of emerging new drugs. The objective of this paper is to introduce tools for using data from social networks to characterize drug abuse. The authors outline a structured approach to analyze social media in order to capture emerging trends in drug abuse. An analysis of social media discussions about drug abuse patterns with computational linguistics, graph theory, and agent-based modeling permits the real-time monitoring and characterization of trends of drugs of abuse. These tools provide a powerful complement to existing methods of toxicovigilance.

O’Connor K, Pimpalkhute P, Nikfarjam A, Ginn R, Smith KL, Gonzalez G. Pharmacovigilance or Twitter? Mining Tweets for Adverse Drug Reactions. AMIA Annu Symp Proc 2014;924-33.

Recent research has shown that Twitter data analytics can have broad implications on public health research. However, its value for pharmacovigilance has been scantly studied – with health related forums and community support groups preferred for the task. The authors present a systematic study of tweets collected for 74 drugs to assess their value as sources of potential signals for adverse drug reactions (ADRs). They created an annotated corpus of 10,822 tweets. Each tweet was annotated for the presence or absence of ADR mentions, with the span and Unified Medical Language System (UMLS) concept ID noted for each ADR present. Using Cohen’s kappa1, we calculated the inter-annotator agreement (IAA) for the binary annotations to be 0.69. To demonstrate the utility of the corpus, we attempted a lexicon-based approach for concept extraction, with promising success (54.1% precision, 62.1% recall, and 57.8% F-measure). A subset of the corpus is freely available at: http://diego.asu.edu/downloads.

Freifeld CC, Brownstein JS, Benone CM, Bao W, Filice R, Kass-Hout T, et al. Digital Drug Safety Surveillance: Monitoring Pharmaceutical Products in Twitter. Drug Saf 2014;37(5):343-50.

Traditional adverse event (AE) reporting systems have been slow in adapting to online AE reporting from patients. In the meantime, increasing numbers of patients have turned to social media to share their experiences with drugs, medical devices, and vaccines. The aim of this study was to evaluate the level of concordance between Twitter posts mentioning AE-like reactions and spontaneous reports received by a regulatory agency. The authors collected public English-language Twitter posts mentioning 23 medical products from 1 November 2012 through 31 May 2013. Data were filtered using a semi-automated process to identify posts with resemblance to AEs (Proto-AEs). A dictionary was developed to translate Internet vernacular to a standardized regulatory ontology for analysis (MedDRA(®)). Aggregated frequency of identified product-event pairs was then compared with data from the public FDA Adverse Event Reporting System (FAERS) by System Organ Class (SOC). Of the 6.9 million Twitter posts collected, 4,401 Proto-AEs were identified out of 60,000 examined. Automated, dictionary-based symptom classification had 86 % recall and 72 % precision [corrected]. Similar overall distribution profiles were observed, with Spearman rank correlation rho of 0.75 (p < 0.0001) between Proto-AEs reported in Twitter and FAERS by SOC. In conclusion, patients reporting AEs on Twitter showed a range of sophistication when describing their experience. Despite the public availability of these data, their appropriate role in pharmacovigilance has not been established. Additional work is needed to improve data acquisition and automation.

Carbonell P, Mayer MA, Bravo A. Exploring Brand-name Drug Mentions on Twitter for Pharmacovigilance. Stud Health Technol Inform 2015;210:55-9.

Twitter has been proposed by several studies as a means to track public health trends such as influenza and Ebola outbreaks by analyzing user messages in order to measure different population features and interests. In this work the authors analyze the number and features of mentions on Twitter of drug brand names in order to explore the potential usefulness of the automated detection of drug side effects and drug-drug interactions on social media platforms such as Twitter. This information can be used for the development of predictive models for drug toxicity, drug-drug interactions or drug resistance. Taking into account the large number of drug brand mentions that we found on Twitter, it is promising as a tool for the detection, understanding and monitoring the way people manage prescribed drugs.

Patel R, Chang T, Greysen SR, Chopra V. Social Media Use in Chronic Disease: A Systematic Review and Novel Taxonomy. Am J Med 2015;128(12):1335-50.

The authors aimed to evaluate clinical outcomes from applications of contemporary social media in chronic disease; to develop a conceptual taxonomy to categorize, summarize, and then analyze the current evidence base; and to suggest a framework for future studies on this topic. They performed a systematic review of MEDLINE via PubMed (January 2000 to January 2015) of studies reporting clinical outcomes on leading contemporary social media (ie, Facebook, Twitter, Wikipedia, YouTube) use in 10 chronic diseases. Of 378 citations identified, 42 studies examining the use of Facebook (n = 16), blogs (n = 13), Twitter (n = 8), wikis (n = 5), and YouTube (n = 4) on outcomes in cancer (n = 14), depression (n = 13), obesity (n = 9), diabetes (n = 4), heart disease (n = 3), stroke (n = 2), and chronic lower respiratory tract infection (n = 1) were included. Studies were classified as support (n = 16), patient education (n = 10), disease modification (n = 6), disease management (n = 5), and diagnosis (n = 5) within our taxonomy. The overall impact of social media on chronic disease was variable, with 48% of studies indicating benefit, 45% neutral or undefined, and 7% suggesting harm. Among studies that showed benefit, 85% used either Facebook or blogs, and 40% were based within the domain of support. The authors concluded that using social media to provide social, emotional, or experiential support in chronic disease, especially with Facebook and blogs, appears most likely to improve patient care.

Coloma PM, Becker B, Sturkenboom MC, van Mulligen EM, Kors JA. Evaluating Social Media Networks in Medicines Safety Surveillance: Two Case Studies. Drug Saf 2015;38(10):921-30.

There is growing interest in whether social media can capture patient-generated information relevant for medicines safety surveillance that cannot be found in traditional sources. The aim of this study was to evaluate the potential contribution of mining social media networks for medicines safety surveillance using the following associations as case studies: (1) rosiglitazone and cardiovascular events (i.e. stroke and myocardial infarction); and (2) human papilloma virus (HPV) vaccine and infertility. The authors collected publicly accessible, English-language posts on Facebook, Google+, and Twitter until September 2014. Data were queried for co-occurrence of keywords related to the drug/vaccine and event of interest within a post. Messages were analysed with respect to geographical distribution, context, linking to other web content, and author’s assertion regarding the supposed association. A total of 2537 posts related to rosiglitazone/cardiovascular events and 2236 posts related to HPV vaccine/infertility were retrieved, with the majority of posts representing data from Twitter (98 and 85%, respectively) and originating from users in the US. Approximately 21% of rosiglitazone-related posts and 84% of HPV vaccine-related posts referenced other web pages, mostly news items, law firms’ websites, or blogs. Assertion analysis predominantly showed affirmation of the association of rosiglitazone/cardiovascular events (72%; n = 1821) and of HPV vaccine/infertility (79%; n = 1758). Only ten posts described personal accounts of rosiglitazone/cardiovascular adverse event experiences, and nine posts described HPV vaccine problems related to infertility. The authors concluded that publicly available data from the considered social media networks were sparse and largely untraceable for the purpose of providing early clues of safety concerns regarding the prespecified case studies. Further research investigating other case studies and exploring other social media platforms are necessary to further characterise the usefulness of social media for safety surveillance.

Alvaro N, Conway M, Doan S, Lofi C, Overington J, Collier N. Crowdsourcing Twitter Annotations to Identify First-hand Experiences of Prescription Drug Use. J Biomed Inform 2015:58:280-7.

Self-reported patient data has been shown to be a valuable knowledge source for post-market pharmacovigilance. In this paper the authors propose using Twitter to gather evidence about adverse drug reactions (ADRs) after firstly having identified micro-blog messages (also know as “tweets”) that report first-hand experience. In order to achieve this goal, they explore machine learning with data crowdsourced from laymen annotators. With the help of lay annotators recruited from CrowdFlower they manually annotated 1548 tweets containing keywords related to two kinds of drugs: SSRIs (eg. Paroxetine), and cognitive enhancers (eg. Ritalin). Results show that inter-annotator agreement (Fleiss’ kappa) for crowdsourcing ranks in moderate agreement with a pair of experienced annotators (Spearman’s Rho=0.471). Authors utilized the gold standard annotations from CrowdFlower for automatically training a range of supervised machine learning models to recognize first-hand experience. F-Score values are reported for 6 of these techniques with the Bayesian Generalized Linear Model being the best (F-Score=0.64 and Informedness=0.43) when combined with a selected set of features obtained by using information gain criteria.
For the task of selecting ADR data on the crowdsourced annotations Bayesian Generalized Linear Model (BGLM) was observed to be the model providing the overall highest F-Score among those tested, only surpassed by C50 when using the top 50% and the 100% of the features, although in terms of Informedness BGLM obtained the best scores all the time.

Nakhasi A, Bell SG, Passarella RJ, Paul MG, Dredze M, Pronovost PJ. The Potential of Twitter as a Data Source for Patient Safety. J Patient Saf 2016; DOI: 10.1097/PTS.0000000000000253.

Error-reporting systems are widely regarded as critical components to improving patient safety, yet current systems do not effectively engage patients. The authors sought to assess Twitter as a source to gather patient perspective on errors in this feasibility study. They included publicly accessible tweets in English from any geography. To collect patient safety tweets, they authors consulted a patient safety expert and constructed a set of highly relevant phrases, such as “doctor screwed up.” then they used Twitter‘s search application program interface from January to August 2012 to identify tweets that matched the set of phrases. Two researchers used criteria to independently review tweets and choose those relevant to patient safety; a third reviewer resolved discrepancies. Variables included source and sex of tweeter, source and type of error, emotional response, and mention of litigation. Of 1006 tweets analyzed, 839 (83%) identified the type of error: 26% of which were procedural errors, 23% were medication errors, 23% were diagnostic errors, and 14% were surgical errors. A total of 850 (84%) identified a tweet source, 90% of which were by the patient and 9% by a family member. A total of 519 (52%) identified an emotional response, 47% of which expressed anger or frustration, 21% expressed humor or sarcasm, and 14% expressed sadness or grief. Of the tweets, 6.3% mentioned an intent to pursue malpractice litigation. The authors concluded that Twitter is a relevant data source to obtain the patient perspective on medical errors. Twitter may provide an opportunity for health systems and providers to identify and communicate with patients who have experienced a medical error. Further research is needed to assess the reliability of the data.

Powell GE, Seifert HA, Reblin T, Burstein PJ, Blowers J, Menius JA, et al. Social Media Listening for Routine Post-Marketing Safety Surveillance. Drug Saf 2016;39(5):443-54.

Limitations of classical data sources for post-market surveillance include potential under-reporting, lack of geographic diversity, and time lag between event occurrence and discovery. There is growing interest in exploring the use of social media (‘social listening‘) to supplement established approaches for pharmacovigilance. Although social listening is commonly used for commercial purposes, there are only anecdotal reports of its use in pharmacovigilance. Health information posted online by patients is often publicly available, representing an untapped source of post-marketing safety data that could supplement data from existing sources. The objective of this paper is to describe one methodology that could help unlock the potential of social media for safety surveillance. A third-party vendor acquired 24 months of publicly available Facebook and Twitter data, then processed the data by standardizing drug names and vernacular symptoms, removing duplicates and noise, masking personally identifiable information, and adding supplemental data to facilitate the review process. The resulting dataset was analyzed for safety and benefit information. In Twitter, a total of 6,441,679 Medical Dictionary for Regulatory Activities (MedDRA(®)) Preferred Terms (PTs) representing 702 individual PTs were discussed in the same post as a drug compared with 15,650,108 total PTs representing 946 individual PTs in Facebook. Further analysis revealed that 26 % of posts also contained benefit information. Authors concluded that social media listening is an important tool to augment post-marketing safety surveillance. Much work remains to determine best practices for using this rapidly evolving data source.

Adrover C, Bodnar T, Huang Z, Telenti A, Salathe M. Identifying Adverse Effects of HIV Drug Treatment and Associated Sentiments Using Twitter. JMIR Public Health Surveill 2015 Jul 27;1(2):e7. doi: 10.2196/publichealth.4488.

Social media platforms are increasingly seen as a source of data on a wide range of health issues. Twitter is of particular interest for public health surveillance because of its public nature. However, the very public nature of social media platforms such as Twitter may act as a barrier to public health surveillance, as people may be reluctant to publicly disclose information about their health. This is of particular concern in the context of diseases that are associated with a certain degree of stigma, such as HIV/AIDS. The objective of the study was to assess whether adverse effects of HIV drug treatment and associated sentiments can be determined using publicly available data from social media. The authors describe a combined approach of machine learning and crowdsourced human assessment to identify adverse effects of HIV drug treatment solely on individual reports posted publicly on Twitter. Starting from a large dataset of 40 million tweets collected over three years, we identify a very small subset (1642; 0.004%) of individual reports describing personal experiences with HIV drug treatment. Despite the small size of the extracted final dataset, the summary representation of adverse effects attributed to specific drugs, or drug combinations, accurately captures well-recognized toxicities. In addition, the data allowed us to discriminate across specific drug compounds, to identify preferred drugs over time, and to capture novel events such as the availability of preexposure prophylaxis. The authors conclude that the effect of limited data sharing due to the public nature of the data can be partially offset by the large number of people sharing data in the first place, an observation that may play a key role in digital epidemiology in general.

Korkcontzelos I, Nikfarjam A, Shardlow M, Sarker A, Ananiadou S, Gonzalez GH. Analysis of the Effect of Sentiment Analysis on Extracting Adverse Drug Reactions from Tweets and Forum Posts. J Biomed Inform 2016;62:148-68.

Based on the intuition that patients post about Adverse Drug Reactions (ADRs) expressing negative sentiments, the authors investigated the effect of sentiment analysis features in locating ADR mentions. To achieve that, the authors enriched the feature space of a state-of-the-art ADR identification method with sentiment analysis features. Using a corpus of posts from the DailyStrength forum and tweets annotated for ADR and indication mentions, they evaluated the extent to which sentiment analysis features help in locating ADR mentions and distinguishing them from indication mentions. Evaluation results show that sentiment analysis features marginally improve ADR identification in tweets and health related forum posts. Adding sentiment analysis features achieved a statistically significant F-measure increase from 72.14% to 73.22% in the Twitter part of an existing corpus using its original train/test split. Using stratified 10×10-fold cross-validation, statistically significant F-measure increases were shown in the DailyStrength part of the corpus, from 79.57% to 80.14%, and in the Twitter part of the corpus, from 66.91% to 69.16%. Moreover, sentiment analysis features are shown to reduce the number of ADRs being recognized as indications. In conclusion, this study shows that adding sentiment analysis features can marginally improve the performance of even a state-of-the-art ADR identification method. This improvement can be of use to pharmacovigilance practice, due to the rapidly increasing popularity of social media and health forums.

Liu J, Zhao S, Zhang X. An Ensemble Method for Extracting Adverse Drug Events from Social Media. Artif Intell Med 2016;70:62-76.

With the development of Web 2.0, social media has become a large data source for information on ADEs. The objective of this study was to develop a relation extraction system that uses natural language processing techniques to effectively distinguish between ADEs and non-ADEs in informal text on social media. The authors developed a feature-based approach that utilizes various lexical, syntactic, and semantic features. Information-gain-based feature selection is performed to address high-dimensional features. Then, they evaluated the effectiveness of four well-known kernel-based approaches (i.e., subset tree kernel, tree kernel, shortest dependency path kernel, and all-paths graph kernel) and several ensembles that are generated by adopting different combination methods (i.e., majority voting, weighted averaging, and stacked generalization). All of the approaches are tested using three data sets: two health-related discussion forums and one general social networking site (i.e., Twitter). When investigating the contribution of each feature subset, the feature-based approach attains the best area under the receiver operating characteristics curve (AUC) values, which are 78.6%, 72.2%, and 79.2% on the three data sets. When individual methods are used, we attain the best AUC values of 82.1%, 73.2%, and 77.0% using the subset tree kernel, shortest dependency path kernel, and feature-based approach on the three data sets, respectively. When using classifier ensembles, we achieve the best AUC values of 84.5%, 77.3%, and 84.5% on the three data sets, outperforming the baselines. In conclusion, the experimental results indicate that ADE extraction from social media can benefit from feature selection. With respect to the effectiveness of different feature subsets, lexical features and semantic features can enhance the ADE extraction capability. Kernel-based approaches, which can stay away from the feature sparsity issue, are qualified to address the ADE extraction problem. Combining different individual classifiers using suitable combination methods can further enhance the ADE extraction effectiveness.

Eshleman R, Singh R. Leveraging Graph Topology and Semantic Context for Pharmacovigilance through Twitter-streams. BMC Bioinformatics 2016;17(Suppl 13):335.

Adverse drug events (ADEs) constitute one of the leading causes of post-therapeutic death and their identification constitutes an important challenge of modern precision medicine. Unfortunately, the onset and effects of ADEs are often underreported complicating timely intervention. At over 500 million posts per day, Twitter is a commonly used social media platform. The ubiquity of day-to-day personal information exchange on Twitter makes it a promising target for data mining for ADE identification and intervention. Three technical challenges are central to this problem: (1) identification of salient medical keywords in (noisy) tweets, (2) mapping drug-effect relationships, and (3) classification of such relationships as adverse or non-adverse. The authors used a bipartite graph-theoretic representation called a drug-effect graph (DEG) for modeling drug and side effect relationships by representing the drugs and side effects as vertices. We construct individual DEGs on two data sources. The first DEG is constructed from the drug-effect relationships found in FDA package inserts as recorded in the SIDER database. The second DEG is constructed by mining the history of Twitter users. We use dictionary-based information extraction to identify medically-relevant concepts in tweets. Drugs, along with co-occurring symptoms are connected with edges weighted by temporal distance and frequency. Finally, information from the SIDER DEG is integrate with the Twitter DEG and edges are classified as either adverse or non-adverse using supervised machine learning.
The authors examined both graph-theoretic and semantic features for the classification task. The proposed approach can identify adverse drug effects with high accuracy with precision exceeding 85 % and F1 exceeding 81 %. When compared with leading methods at the state-of-the-art, which employ un-enriched graph-theoretic analysis alone, our method leads to improvements ranging between 5 and 8 % in terms of the aforementioned measures. Additionally, we employ our method to discover several ADEs which, though present in medical literature and Twitter-streams, are not represented in the SIDER databases. In conclusion, the authors present a DEG integration model as a powerful formalism for the analysis of drug-effect relationships that is general enough to accommodate diverse data sources, yet rigorous enough to provide a strong mechanism for ADE identification.

Koutkias VG, Lillo-le-Louet A, Jaulent MC. Exploiting Heterogeneous Publicly Available Data Sources for Drug Safety Surveillance: Computational Framework and Case Studies. Expert Opin Drug Saf 2017;16(2):113-24.

In this article, the authors introduce and validate a computational framework exploiting dominant as well as emerging publicly available data sources for drug safety surveillance. Their approach relies on appropriate query formulation for data acquisition and subsequent filtering, transformation and joint visualization of the obtained data. Data from the FDA Adverse Event Reporting System (FAERS), PubMed and Twitter were used. In order to assess the validity and the robustness of the approach, the authors elaborated on two important case studies, namely, clozapine-induced cardiomyopathy/myocarditis versus haloperidol-induced cardiomyopathy/myocarditis, and apixaban-induced cerebral hemorrhage.
The analysis of the obtained data provided interesting insights (identification of potential patient and health-care professional experiences regarding ADRs in Twitter, information/arguments against an ADR existence across all sources), while illustrating the benefits (complementing data from multiple sources to strengthen/confirm evidence) and the underlying challenges (selecting search terms, data presentation) of exploiting heterogeneous information sources, thereby advocating the need for the proposed framework. The authors concluded that this work contributes in establishing a continuous learning system for drug safety surveillance by exploiting heterogeneous publicly available data sources via appropriate support tools.

Pierce CE, Bouri K, Pamer C, Proestel S, Rodriguez HW, Van Le H, et al. Evaluation of Facebook and Twitter Monitoring to Detect Safety Signals for Medical Products: An Analysis of Recent FDA Safety Alerts. Drug Saf 2017;40(4):317-31.

The rapid expansion of the Internet and computing power in recent years has opened up the possibility of using social media for pharmacovigilance. While this general concept has been proposed by many, central questions remain as to whether social media can provide earlier warnings for rare and serious events than traditional signal detection from spontaneous report data. The objective was to examine whether specific product-adverse event pairs were reported via social media before being reported to the US FDA Adverse Event Reporting System (FAERS). A retrospective analysis of public Facebook and Twitter data was conducted for 10 recent FDA postmarketing safety signals at the drug-event pair level with six negative controls. Social media data corresponding to two years prior to signal detection of each product-event pair were compiled. Automated classifiers were used to identify each ‘post with resemblance to an adverse event’ (Proto-AE), among English language posts. A custom dictionary was used to translate Internet vernacular into Medical Dictionary for Regulatory Activities (MedDRA®) Preferred Terms. Drug safety physicians conducted a manual review to determine causality using World Health Organization-Uppsala Monitoring Centre (WHO-UMC) assessment criteria. Cases were also compared with those reported in FAERS.
A total of 935,246 posts were harvested from Facebook and Twitter, from March 2009 through October 2014. The automated classifier identified 98,252 Proto-AEs. Of these, 13 posts were selected for causality assessment of product-event pairs. Clinical assessment revealed that posts had sufficient information to warrant further investigation for two possible product-event associations: dronedarone-vasculitis and Banana Boat Sunscreen–skin burns. No product-event associations were found among the negative controls. In one of the positive cases, the first report occurred in social media prior to signal detection from FAERS, whereas the other case occurred first in FAERS.
In conclusion, an efficient semi-automated approach to social media monitoring may provide earlier insights into certain adverse events. More work is needed to elaborate additional uses for social media data in pharmacovigilance and to determine how they can be applied by regulatory agencies.

Cocos A, Fiks AG, Masino AJ. Deep Learning for Pharmacovigilance: Recurrent Neural Network Architectures for Labeling Adverse Drug Reactions in Twitter Posts. J Am Med Inform Assoc 2017;24(4):813-21.

Social media is an important pharmacovigilance data source for adverse drug reaction (ADR) identification. As human review is infeasible due to data quantity, natural language processing techniques are necessary. Social media includes informal vocabulary and irregular grammar, which challenge natural language processing methods. The objective of this study was to develop a scalable, deep-learning approach that exceeds state-of-the-art ADR detection performance in social media. The authors developed a recurrent neural network (RNN) model that labels words in an input sequence with ADR membership tags. The only input features are word-embedding vectors, which can be formed through task-independent pretraining or during ADR detection training.
Our best-performing RNN model used pretrained word embeddings created from a large, non-domain-specific Twitter dataset. It achieved an approximate match F-measure of 0.755 for ADR identification on the dataset, compared to 0.631 for a baseline lexicon system and 0.65 for the state-of-the-art conditional random field model. Feature analysis indicated that semantic information in pretrained word embeddings boosted sensitivity and, combined with contextual awareness captured in the RNN, precision.
Our model required no task-specific feature engineering, suggesting generalizability to additional sequence-labeling tasks. Learning curve analysis showed that our model reached optimal performance with fewer training examples than the other models.
In conclusion, ADR detection performance in social media is significantly improved by using a contextually aware model and word embeddings formed from large, unlabeled datasets. The approach reduces manual data-labeling requirements and is scalable to large social media datasets.

Salathe M. Digital Pharmacovigilance and Disease Surveillance: Combining Traditional and Big-Data Systems for Better Public Health. J Infect Dis 2016:214(suppl_4):S399-S403.

The digital revolution has contributed to very large data sets (ie, big data) relevant for public health. The two major data sources are electronic health records from traditional health systems and patient-generated data. As the two data sources have complementary strengths-high veracity in the data from traditional sources and high velocity and variety in patient-generated data-they can be combined to build more-robust public health systems. However, they also have unique challenges. Patient-generated data in particular are often completely unstructured and highly context dependent, posing essentially a machine-learning challenge. Some recent examples from infectious disease surveillance and adverse drug event monitoring demonstrate that the technical challenges can be solved. Despite these advances, the problem of verification remains, and unless traditional and digital epidemiologic approaches are combined, these data sources will be constrained by their intrinsic limits.

Comfort S, Perera S, Hudson Z, Dorrell D, Meireis S, Nagarajan M, et al. Sorting Through the Safety Data Haystack: Using Machine Learning to Identify Individual Case Safety Reports in Social-Digital Media. Drug Saf 2018;doi: 10.1007/s40264-018-0641-7.

There is increasing interest in social digital media (SDM) as a data source for pharmacovigilance activities; however, SDM is considered a low information content data source for safety data. Given that pharmacovigilance itself operates in a high-noise, lower-validity environment without objective ‘gold standards’ beyond process definitions, the introduction of large volumes of SDM into the pharmacovigilance workflow has the potential to exacerbate issues with limited manual resources to perform adverse event identification and processing. Recent advances in medical informatics have resulted in methods for developing programs which can assist human experts in the detection of valid individual case safety reports (ICSRs) within SDM. The objective of this study was to develop rule-based and machine learning (ML) models for classifying ICSRs from SDM and compared their performance with that of human pharmacovigilance experts. The authors used a random sampling from a collection of 311,189 SDM posts that mentioned Roche products and brands in combination with common medical and scientific terms sourced from Twitter, Tumblr, Facebook, and a spectrum of news media blogs to develop and evaluate three iterations of an automated ICSR classifier. The ICSR classifier models consisted of sub-components to annotate the relevant ICSR elements and a component to make the final decision on the validity of the ICSR. Agreement with human pharmacovigilance experts was chosen as the preferred performance metric and was evaluated by calculating the Gwet AC1 statistic (gKappa). The best performing model was tested against the Roche global pharmacovigilance expert using a blind dataset and put through a time test of the full 311,189-post dataset.
During this effort, the initial strict rule-based approach to ICSR classification resulted in a model with an accuracy of 65% and a gKappa of 46%. Adding an ML-based adverse event annotator improved the accuracy to 74% and gKappa to 60%. This was further improved by the addition of an additional ML ICSR detector. On a blind test set of 2500 posts, the final model demonstrated a gKappa of 78% and an accuracy of 83%. In the time test, it took the final model 48 h to complete a task that would have taken an estimated 44,000 h for human experts to perform.
In conclusion, the results of this study indicate that an effective and scalable solution to the challenge of ICSR detection in SDM includes a workflow using an automated ML classifier to identify likely ICSRs for further human SME review.

 

Filed Under: Twitter Tagged With: adverse drug reaction, adverse event, data mining, safety surveillance, social media, twitter

Real World Evidence (RWE): Predictive Analytics to Impact Patient Safety

February 23, 2018 by Ale Vazquez-Gragg, MD Leave a Comment

Real World Evidence and SafetyThe use of new analytical tools applied to large, diverse, complex data sets, so called “big data”, the development of devices to track and gather real-time healthcare data and information and the use of digital media are on the increase in the healthcare environment and have the potential to be of great value if harnessed and utilized appropriately.

The current main system for keeping track of dangerous side effects of prescription drugs is deeply flawed according to the Institute for Safe Medications Practices (ISMP). The study conducted by the ISMP found that only about half of reports of serious side effects submitted by manufacturers met basic standards for completeness.

No longer just a sideline used to fill knowledge gaps, the evidence generated from real-world data (RWD) is rapidly becoming an integral component of product evidence strategies. However, the growing volumes and heterogeneity of real-world data sources are creating increasingly inefficient and chaotic analytic environments and as a result, new approaches for database analyses are needed.

Real-world evidence (RWE) allows companies to make more informed and reliable strategic decisions earlier when it comes to protect patient safety. Another value added is converting RWD into valuable RWE that offers great scientific and patient benefit trials as well as shortening phase III to accelerate the approval procedures. These benefits include improving the ability to positively impact patient outcomes through understanding of disease characteristics and treatment patterns, enhancing medicines compliance and aiding in interpreting treatment outcomes for individual patients. It also enables organizations to demonstrate health outcomes and support the case for the value of their products to health authorities, payers, health care providers and patients.

The opportunity to use these technologies and derive their potential benefits to assess the efficacy and effectiveness of therapeutic options is in its infancy. Although they could be the key to establishing a credible new generation of fit-for-purpose RWE, pharmaceutical companies have been cautiously investigating the use of the various technologies that contribute to big data collection (e.g., social media, electronic health records, insurance data claim databases) and the application of analytics to these data sets including pharmacovigilance to assess side effects. The case for accessing and evaluating the plethora of different data sets is clear. However, while the opportunity is large, exploiting the value requires the appropriate governance, knowledge and analytics capabilities to stay within the acceptable tolerance levels for compliance and reputation risk management. Pharmacovigilance departments will become a key element within the organizations.

Questions have been raised as to how best to deploy innovative collection and analytic technologies to maximize their effectiveness. Approaches such as the Advancing Medical Innovation initiative encourage the FDA to identify opportunities to use big data to streamline and support pre- and post-approval activities. In Europe, collaborative projects in the area of post-authorization efficacy studies have identified the need for companies and agencies to be able to measure safety and effectiveness in the real-world use of new medicines.

This is mirrored by the need for pharmaceutical and biotech companies to quickly adapt their pharmacovigilance departments into cross-functional teams to strategically make decisions regarding how new medicines will be used in the real world and to confirm the expected benefit and value, often derived largely from controlled clinical studies. In a rapidly changing regulatory environment, with a diverse set of data sources, contractual mechanisms and data privacy requirements, the capabilities needed to extract the value from the data become strategic in their own right.

Over the last years the potential of real-world data and analytics has been discussed as opportunities to enhance patient engagement, reduce uncertainty in the development and approval space, as well to serve as a natural process for the collection of benefit and risk data post-authorization. Collecting data from a mix of evidentiary experiences would support novel flexible regulatory pathways that accelerate reviews and access to medicines, and therefore, will likely play a key role in transforming medicine development and access over the next decade.

Are we ready for this?

Filed Under: Opinion

Big Data and Pharmacovigilance: Where are We Going?

January 26, 2018 by Jose Rossello Leave a Comment

Everyone talks about “big data”, and how it is going to transform many industries, including healthcare. In a recent work, Bate, Reynolds, and Caubel analyze and describe the achievements of big data approaches in pharmacoepidemiology, improvement on quality of data for drug safety research, and the role of big data in relation to the identification of potential safety signals in post-market surveillance, that is, the impact of big data on quantitative signal evaluation and the identification of potentially new safety signals.

In pharmacovigilance and signal detection, we have moved quickly from manual, paper-based methods for signal detection to spontaneous reporting systems that require electronic submission, but allow quantitative and qualitative analyses as part of signal management systems.

According to the authors:

While the core of regulated pharmacovigilance practice still centers on the collection of individual case safety reports, change is occurring, in part as a result of Big Data approaches. The greatest change in pharmacovigilance analytics being applied today, and the one most connected to the Big Data revolution, is the more sophisticated use of observational data, as evidenced by pharmacoepidemiologic studies conducted across multiple databases and the development of large networks of observational databases of Electronic Healthcare Records in North America.

The new pharmacovigilance analytics will go beyond safety assessment. It will provide value for research too. Examples will be comparative effectiveness studies, pragmatic trials or investigational trials in real-world settings. The FDA Sentinel Initiative is a clear example of this new approach.

The authors also talk about what is known as hypothesis-free signal detection with its advantages and limitations, consumer wearable technology for pharmacoepidemiologic research, the new data streams and technologies as a source for identifying potential new safety signals, and the need to critically evaluate the impact of innovative data sources and techniques.

For more information check out the complete article from Therapeutic Advances in Drug Safety.

Read the source article here: The hope, hype and reality of Big Data for pharmacovigilance.

Filed Under: Big Data

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