Systematic review on the prevalence, frequency and comparative value of adverse events data in social media

Aim: The aim of this review was to summarize the prevalence, frequency and comparative value of information on the adverse events of healthcare interventions from user comments and videos in social media. Methods: A systematic review of assessments of the prevalence or type of information on adverse events in social media was undertaken. Sixteen databases and two internet search engines were searched in addition to handsearching, reference checking and contacting experts. The results were sifted independently by two researchers. Data extraction and quality assessment were carried out by one researcher and checked by a second. The quality assessment tool was devised in-house and a narrative synthesis of the results followed. Results: From 3064 records, 51 studies met the inclusion criteria. The studies assessed over 174 social media sites with discussion forums (71%) being the most popular. The overall prevalence of adverse events reports in social media varied from 0.2% to 8% of posts. Twenty-nine studies compared the results from searching social media with using other data sources to identify adverse events. There was general agreement that a higher frequency of adverse events was found in social media and that this was particularly true for ‘symptom’ related and ‘mild’ adverse events. Those adverse events that were under-represented in social media were laboratory-based and serious adverse events. Conclusions: Reports of adverse events are identifiable within social media. However, there is considerable heterogeneity in the frequency and type of events reported, and the reliability or validity of the data has not been thoroughly evaluated

Harnessing Machine Learning to Improve Healthcare Monitoring with FAERS

This research study investigates the potential of machine learning techniques to improve healthcare monitoring through the utilization of data from the FDA Adverse Event Reporting System (FAERS). The objective is to explore specific applications of machine learning in healthcare monitoring with FAERS and highlight their findings. The study reveals several significant ways in which machine learning can contribute to enhancing healthcare monitoring using FAERS.Machine learning algorithms can detect potential safety signals at an early stage by analyzing FAERS data. By employing anomaly detection and temporal pattern analysis techniques, these models can identify emerging safety concerns that were previously unknown or underreported. This early detection enables timely action to mitigate risks associated with medications or medical products.Machine learning models can assist in pharmacovigilance triage, addressing the challenge posed by the large number of adverse event reports within FAERS. By developing ranking and classification models, adverse events can be prioritized based on severity, novelty, or potential impact. This automation of the triage process enables pharmacovigilance teams to efficiently identify and investigate critical safety concerns.Machine learning models can automate the classification and coding of adverse events, which are often present in unstructured text within FAERS reports. Through the application of Natural Language Processing (NLP) techniques, such as named entity recognition and text classification, relevant information can be extracted, enhancing the efficiency and accuracy of adverse event coding.Machine learning algorithms can refine and validate signals generated from FAERS data by incorporating additional data sources, such as electronic health records, social media, or clinical trials data. This integration provides a more comprehensive understanding of potential risks and helps filter out false positives, facilitating the identification of signals requiring further investigation.Machine learning enables real-time surveillance of FAERS data, allowing for the identification of safety concerns as they occur. Continuous monitoring and real-time analysis of incoming reports enable machine learning models to trigger alerts or notifications to relevant stakeholders, promoting timely intervention to minimize patient harm.The study demonstrates the use of machine learning models to conduct comparative safety analyses by combining FAERS data with other healthcare databases. These models assist in identifying safety differences between medications, patient populations, or dosing regimens, enabling healthcare providers and regulators to make informed decisions regarding treatment choices.While machine learning is a powerful tool in healthcare monitoring, its implementation should be complemented by human expertise and domain knowledge. The interpretation and validation of results generated by machine learning models necessitate the involvement of healthcare professionals and pharmacovigilance experts to ensure accurate and meaningful insights.This research study illustrates the diverse applications of machine learning in improving healthcare monitoring using FAERS data. The findings highlight the potential of machine learning in early safety signal detection, pharmacovigilance triage, adverse event classification and coding, signal refinement and validation, real-time surveillance and alerting, and comparative safety analysis. The study emphasizes the importance of combining machine learning with human expertise to achieve effective and reliable healthcare monitoring

Data Mining Techniques in Pharmacovigilance: Analysis of the Publicly Accessible FDA Adverse Event Reporting System (AERS)

Pharmacovigilance is a clinically oriented discipline, which may guide appropriate drug use through a balanced assessment of drug safety. Although much has been done in recent years, efforts are needed to expand the border of pharmacovigilance. We have provided insight into the FDA_Adverse Events Reporting Systems (FDA_AERS), a worldwide publicly available pharmacovigilance archive, to exemplify how to address major methodological issues. We believe that fostering discussion among researchers will increase transparency and facilitate definition of the most reliable approaches. By virtue of its large population coverage and free availability, the FDA_AERS has the potential to pave the way to a new way of looking to signal detection in PhV. Our key messages are: (1) before applying statistical tools (i.e., Data Mining Approaches - DMAs) to pharmacovigilance database for signal detection, all aspects related to data quality should be considered (e.g., drug mapping, missing data and duplicates); (2) at present, the choice of a given DMA mostly relies on local habits, expertise and attitude and there is room for improvement in this area; (3) DMA performance may be highly situation dependent; (4) over-reliance on these methods may have deleterious consequences, especially with the so-called "designated medical events", for which a case-by-case analysis is mandatory and complements disproportionality; and (5) the most appropriate selection of pharmacovigilance tools needs to be tailored to each situation, being mindful of the numerous biases and confounders that may influence performance and incremental utility of DMAs

Opinion Mining and Sentiment Analysis of Online Drug Reviews as a Pharmacovigilance Technique

Pharmacovigilance is the science that focuses on identification and characterization of adverse effects of medications in populations when released to market. The focus of this paper is to study the prospects of exploiting drug related online reviews contributed by social media groups for finding the adverse effects of drugs using opinion mining and sentiment analysis. The experiences and opinions related to drug adverse reactions by patients or other contributors in these forums can be mined and analyzed as a facilitator for pharmacovigilance. This review paper highlights the usability of opinion mining and sentiment analysis as one of the approaches for pharmacovigilance. DOI: 10.17762/ijritcc2321-8169.150711

BIG DATA ANALYTICS IN PHARMACOVIGILANCE - A GLOBAL TREND

Big data analysis has enhanced its demand nowadays in various sectors of health-care including pharmacovigilance. The exact definition of big data is not known to many people though it is routinely used by them. Big data refer to immense and voluminous computerized medical information which are obtained from electronic health records, administrative data, registries related to disease, drug monitoring, etc. This data are usually collected from doctors and pharmacists in a health-care facility. Analysis of big data in pharmacovigilance is useful for early raising of safety alerts, line listing them for signal detection of drugs and vaccines, and also for their validation. The present paper is intended to discuss big data analytics in pharmacovigilance focusing on global prospect and domestic country-India

How Can We Move Clinical Genomics Beyond the Hype?

Examines the debate over increased use of genetic testing, due in part to lax regulation, and its consequences: wasteful spending, patient harm, and health system challenges. Makes recommendations for implementation of and data on promising technologies