The underreporting of adverse drug reactions (ADRs) through traditional reporting channels is a limitation in the efficiency of the current pharmacovigilance system. Patients' experiences with drugs that they report on social media represent a new source of data that may have some value in postmarketing safety surveillance.
Automatic monitoring of Adverse Drug Reactions (ADRs), defined as adverse patient outcomes caused by medications, is a challenging research problem that is currently receiving significant attention from the medical informatics community. In recent years, user-posted data on social media, primarily due to its sheer volume, has become a useful resource for ADR monitoring. Research using social media data has progressed using various data sources and techniques, making it difficult to compare distinct systems and their performances. In this paper, we perform a methodical review to characterize the different approaches to ADR detection/extraction from social media, and their applicability to pharmacovigilance. In addition, we present a potential systematic pathway to ADR monitoring from social media.
Rapid dissemination of information regarding adverse drug reactions is a key aspect for improving pharmacovigilance. There is a possibility that unknown adverse drug reactions will become apparent through post-marketing administration. Currently, although there have been studies evaluating the relationships between a drug and adverse drug reactions using the JADER database which collects reported spontaneous adverse drug reactions, an efficient approach to assess the association between adverse drug reactions of drugs with the same indications as well as the influence of demographics (e.g. gender) has not been proposed.
- Journal of the American Medical Informatics Association : JAMIA
- Published almost 7 years ago
Adverse drug events cause substantial morbidity and mortality and are often discovered after a drug comes to market. We hypothesized that Internet users may provide early clues about adverse drug events via their online information-seeking. We conducted a large-scale study of Web search log data gathered during 2010. We pay particular attention to the specific drug pairing of paroxetine and pravastatin, whose interaction was reported to cause hyperglycemia after the time period of the online logs used in the analysis. We also examine sets of drug pairs known to be associated with hyperglycemia and those not associated with hyperglycemia. We find that anonymized signals on drug interactions can be mined from search logs. Compared to analyses of other sources such as electronic health records (EHR), logs are inexpensive to collect and mine. The results demonstrate that logs of the search activities of populations of computer users can contribute to drug safety surveillance.
Introduction: Drug safety surveillance strongly depends on the spontaneous reporting of adverse drug reactions (ADRs). A major limiting factor of spontaneous reporting systems is underreporting (UR) which describes incorrectly low reporting rates of ADRs. Factors contributing to UR are numerous and feature country-dependent differences. Understanding causes of and factors associated with UR is necessary to facilitate targeted interventions to improve ADR reporting and pharmacovigilance. Methods: A cross-sectional questionnaire-based telephone survey was performed among physicians in outpatient care in a federal state of Germany. Results: From n=316 eligible physicians n=176 completed the questionnaire (response rate=55.7%). Most of the physicians (n=137/77.8%) stated that they report ADRs which they have observed to the competent authority rarely (n=59/33.5%), very rarely (n=59/33.5%) or never (n=19/10.8%); the majority (n=123/69.9%) had not reported any ADRs in 2014. Frequent subjective reasons for non-reporting of ADR were (specified response options): lack of time (n=52/29.5%), the subjective evaluation that the required process of reporting is complicated (n=47/26.7%) or requires too much time (n=25/14.2%) or the assessment that reporting of an ADR is needless (n=22/12.5%); within open answers the participants frequently stated that they do not report ADRs that are already known (n=72/40.9%) and they only report severe ADRs (n=46/26.1%). Discussion: Our results suggest a need to inform physicians about pharmacovigilance and to modify the required procedure of ADR reporting or to offer other reporting options.
Adverse drug reactions (ADRs) are an important cause of morbidity and mortality. Classical Pharmacovigilance process is limited by underreporting which justifies the current interest in new knowledge sources such as social media. The Adverse Drug Reactions from Patient Reports in Social Media (ADR-PRISM) project aims to extract ADRs reported by patients in these media. We identified 5 major challenges to overcome to operationalize the analysis of patient posts: (1) variable quality of information on social media, (2) guarantee of data privacy, (3) response to pharmacovigilance expert expectations, (4) identification of relevant information within Web pages, and (5) robust and evolutive architecture.
- Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society
- Published over 3 years ago
Basic essence of Pharmacovigilance is prevention of ADRs and its precise diagnosis is crucially a primary step, which still remains a challenge among clinicians.
Background Information about safety issues from use of asthma medications in children is limited. Spontaneous adverse drug reaction (ADR) reports can provide information about serious and rarely occurring ADRs in children. Objective To characterize paediatric ADRs reported for asthma medications licensed for paediatric use. Setting Spontaneous ADR reports located in the European ADR database, EudraVigilance. Method ADRs reported for asthma medications licensed for paediatric use from 2007 to 2011 were analysed. The included substances were beclometasone, budesonide, fenoterol, fluticasone, formoterol, mometasone, montelukast, salbutamol and terbutaline and the combinations of budesonide/formoterol, fenoterol/ipratropium and fluticasone/salmeterol. Main outcome measures Reported ADRs were categorized with respect to distribution on age, sex, type and seriousness of reported ADRs, medications and type of reporter. The unit of analysis was one ADR. Results We located 326 spontaneous reports corresponding to 774 ADRs for the included asthma medications. Approximately 85 % of reported ADRs were serious including six fatal cases. In total, 57 % of ADRs were reported for boys. One quarter of all ADRs occurred in children up to 1 year of age. Physicians reported the majority of ADRs. Across medicines, the majority of reported ADRs were of the type “psychiatric disorders” (13 % of total ADRs), followed by “respiratory, thoracic and mediastinal disorders” (10 % of total ADRs) and “skin and subcutaneous disorders” (9 % of total ADRs). The largest number of ADRs was reported for budesonide (21 % of total ADRs), followed by salbutamol (20 % of total ADRs) and fluticasone (19 % of total ADRs). For salbutamol, the largest numbers of serious ADRs were “tachycardia”, “accidental exposure/incorrect dose administered” and “respiratory failure”. Conclusion Only a few ADRs from use of asthma medications in children were identified in the EudraVigilance ADR database, but a large majority of these were serious including fatal cases.
Knowledge of drug safety in the pediatric population of China is limited. This study was designed to evaluate ADRs in children reported to the spontaneous reporting system (SRS) of Shanghai in 2009.
The sheer amount of information about potential adverse drug events published in medical case reports posemajor challenges for drug safety experts to perform timely monitoring. Efficient strategies for identificationand extraction of information about potential adverse drug events from free-text resources are needed tosupport pharmacovigilance research and pharmaceutical decision making. Therefore, this work focusses onthe adaptation of a machine learning-based system for the identification and extraction of potential adversedrug event relations from MEDLINE case reports. It relies on a high quality corpus that was manuallyannotated using an ontology-driven methodology. Qualitative evaluation of the system showed robust results.An experiment with large scale relation extraction from MEDLINE delivered under-identified potentialadverse drug events not reported in drug monographs. Overall, this approach provides a scalableauto-assistance platform for drug safety professionals to automatically collect potential adverse drug eventscommunicated as free-text data.