scholarly journals The Adverse Drug Reactions From Patient Reports in Social Media Project: Protocol for an Evaluation Against a Gold Standard

10.2196/11448 ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. e11448
Author(s):  
Armelle Arnoux-Guenegou ◽  
Yannick Girardeau ◽  
Xiaoyi Chen ◽  
Myrtille Deldossi ◽  
Rim Aboukhamis ◽  
...  
Keyword(s):  
Social Media ◽  
Adverse Drug Reactions ◽  
Gold Standard ◽  
Drug Reactions ◽  
Patient Reports ◽  
Media Project

scholarly journals The Adverse Drug Reactions from Patient Reports in Social Media Project: Five Major Challenges to Overcome to Operationalize Analysis and Efficiently Support Pharmacovigilance Process

2017 ◽  
Vol 6 (9) ◽  
pp. e179 ◽  
Author(s):  
Cedric Bousquet ◽  
Badisse Dahamna ◽  
Sylvie Guillemin-Lanne ◽  
Stefan J Darmoni ◽  
Carole Faviez ◽  
...  
Keyword(s):  
Social Media ◽  
Adverse Drug Reactions ◽  
Drug Reactions ◽  
Patient Reports ◽  
Media Project

scholarly journals The Adverse Drug Reactions From Patient Reports in Social Media Project: Protocol for an Evaluation Against a Gold Standard (Preprint)

2018 ◽  
Author(s):  
Armelle Arnoux-Guenegou ◽  
Yannick Girardeau ◽  
Xiaoyi Chen ◽  
Myrtille Deldossi ◽  
Rim Aboukhamis ◽  
...  
Keyword(s):  
Social Media ◽  
Information Extraction ◽  
Drug Safety ◽  
Gold Standard ◽  
Evaluation Methodology ◽  
Drug Reactions ◽  
Patient Reports ◽  
Safety Surveillance ◽  
Drug Safety Surveillance ◽  
Media Project

BACKGROUND Social media is a potential source of information on postmarketing drug safety surveillance that still remains unexploited nowadays. Information technology solutions aiming at extracting adverse reactions (ADRs) from posts on health forums require a rigorous evaluation methodology if their results are to be used to make decisions. First, a gold standard, consisting of manual annotations of the ADR by human experts from the corpus extracted from social media, must be implemented and its quality must be assessed. Second, as for clinical research protocols, the sample size must rely on statistical arguments. Finally, the extraction methods must target the relation between the drug and the disease (which might be either treated or caused by the drug) rather than simple co-occurrences in the posts. OBJECTIVE We propose a standardized protocol for the evaluation of a software extracting ADRs from the messages on health forums. The study is conducted as part of the Adverse Drug Reactions from Patient Reports in Social Media project. METHODS Messages from French health forums were extracted. Entity recognition was based on Racine Pharma lexicon for drugs and Medical Dictionary for Regulatory Activities terminology for potential adverse events (AEs). Natural language processing–based techniques automated the ADR information extraction (relation between the drug and AE entities). The corpus of evaluation was a random sample of the messages containing drugs and/or AE concepts corresponding to recent pharmacovigilance alerts. A total of 2 persons experienced in medical terminology manually annotated the corpus, thus creating the gold standard, according to an annotator guideline. We will evaluate our tool against the gold standard with recall, precision, and f-measure. Interannotator agreement, reflecting gold standard quality, will be evaluated with hierarchical kappa. Granularities in the terminologies will be further explored. RESULTS Necessary and sufficient sample size was calculated to ensure statistical confidence in the assessed results. As we expected a global recall of 0.5, we needed at least 384 identified ADR concepts to obtain a 95% CI with a total width of 0.10 around 0.5. The automated ADR information extraction in the corpus for evaluation is already finished. The 2 annotators already completed the annotation process. The analysis of the performance of the ADR information extraction module as compared with gold standard is ongoing. CONCLUSIONS This protocol is based on the standardized statistical methods from clinical research to create the corpus, thus ensuring the necessary statistical power of the assessed results. Such evaluation methodology is required to make the ADR information extraction software useful for postmarketing drug safety surveillance. INTERNATIONAL REGISTERED REPOR RR1-10.2196/11448

scholarly journals Normalization of Long-tail Adverse Drug Reactions in Social Media

2020 ◽  
Author(s):  
Emmanouil Manousogiannis ◽  
Sepideh Mesbah ◽  
Alessandro Bozzon ◽  
Robert-Jan Sips ◽  
Zoltan Szlanik ◽  
...  
Keyword(s):  
Social Media ◽  
Adverse Drug Reactions ◽  
Drug Reactions ◽  
Long Tail

scholarly journals Early Detection of Adverse Drug Reactions in Social Health Networks: A Natural Language Processing Pipeline for Signal Detection (Preprint)

2018 ◽  
Author(s):  
Azadeh Nikfarjam ◽  
Julia D Ransohoff ◽  
Alison Callahan ◽  
Erik Jones ◽  
Brian Loew ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Adverse Drug Reactions ◽  
Language Processing ◽  
Signal Generation ◽  
Social Health ◽  
Drug Reactions ◽  
Health Networks ◽  
Health Concerns ◽  
Patient Reports

BACKGROUND Adverse drug reactions (ADRs) occur in nearly all patients on chemotherapy, causing morbidity and therapy disruptions. Detection of such ADRs is limited in clinical trials, which are underpowered to detect rare events. Early recognition of ADRs in the postmarketing phase could substantially reduce morbidity and decrease societal costs. Internet community health forums provide a mechanism for individuals to discuss real-time health concerns and can enable computational detection of ADRs. OBJECTIVE The goal of this study is to identify cutaneous ADR signals in social health networks and compare the frequency and timing of these ADRs to clinical reports in the literature. METHODS We present a natural language processing-based, ADR signal-generation pipeline based on patient posts on Internet social health networks. We identified user posts from the Inspire health forums related to two chemotherapy classes: erlotinib, an epidermal growth factor receptor inhibitor, and nivolumab and pembrolizumab, immune checkpoint inhibitors. We extracted mentions of ADRs from unstructured content of patient posts. We then performed population-level association analyses and time-to-detection analyses. RESULTS Our system detected cutaneous ADRs from patient reports with high precision (0.90) and at frequencies comparable to those documented in the literature but an average of 7 months ahead of their literature reporting. Known ADRs were associated with higher proportional reporting ratios compared to negative controls, demonstrating the robustness of our analyses. Our named entity recognition system achieved a 0.738 microaveraged F-measure in detecting ADR entities, not limited to cutaneous ADRs, in health forum posts. Additionally, we discovered the novel ADR of hypohidrosis reported by 23 patients in erlotinib-related posts; this ADR was absent from 15 years of literature on this medication and we recently reported the finding in a clinical oncology journal. CONCLUSIONS Several hundred million patients report health concerns in social health networks, yet this information is markedly underutilized for pharmacosurveillance. We demonstrated the ability of a natural language processing-based signal-generation pipeline to accurately detect patient reports of ADRs months in advance of literature reporting and the robustness of statistical analyses to validate system detections. Our findings suggest the important contributions that social health network data can play in contributing to more comprehensive and timely pharmacovigilance.

The role of advanced technologies supplemented with traditional methods in pharmacovigilance sciences

2020 ◽  
Vol 14 ◽  
Author(s):  
M Vijaya Satwika Naidu ◽  
Dudala Sai Sushma ◽  
Varun Jaiswal ◽  
S. Asha ◽  
Tarun Pal
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Adverse Drug Reactions ◽  
Mobile Apps ◽  
Health Sector ◽  
Early Developmental Stage ◽  
Drug Reactions ◽  
Traditional Methods ◽  
Advanced Technologies

Background: The immediate automatic systemic monitoring and reporting of adverse drug reaction, improving the efficacy is the utmost need of medical informatics community. The venturing of advanced digital technologies into the health sector has opened new avenues for rapid monitoring. In recent years, data shared through social media, mobile apps and on other social websites has increased manifolds requiring data mining techniques. Objective: The objective of this report is to highlight the role of advanced technologies together with traditional methods to proactively aid in early detection of adverse drug reactions concerned with drug safety and pharmacovigilance. Methods: A thorough search was conducted for papers and patents regarding pharmacivigilance. All articles with respect to relevant subject were explored and mined from public repositories such as Pubmed, Google Scholar, Springer, ScienceDirect (Elsevier), Web of Science, etc. Results: The European Union’s Innovative Medicines Initiative WEB-RADR project emphasized the development of mobile applications and social media data for reporting adverse effects. Only relevant data has to be captured through the data mining algorithms (DMAs) playing an important role in timely prediction of risk with high accuracy using two popular approaches the frequentist and Bayesian approach. The pharmacovigilance at premarketing stage is useful for the prediction of the adverse drug reactions in early developmental stage of a drug. Later postmarketing safety reports and clinical data reports are important to be monitored through electronic health records, prescription-event monitoring, spontaneous reporting databases, etc approaches. Conclusion: The advanced technologies supplemented with traditional technologies is the need of hour for evaluating product’s risk profile and reducing risk in population esp. with comorbid conditions and on concomitant medications.

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