The promise of augmenting pharmacovigilance with patient-generated data drawn from the Internet was called out by a scientific committee charged with conducting a review of the current and planned pharmacovigilance practices of the US Food and Drug Administration (FDA). To this end, we present a study on harnessing behavioral data drawn from Internet search logs to detect adverse drug reactions...

In recent years, pharmacovigilance has undergone some major changes. First, the patient's active role in identifying and describing adverse drug reactions (ADRs) has gained recognition. Second, pharmacovigilance has increasingly incorporated information and communications technology (ICT). Patients can now upload their own reports of ADRs online. Data on intensive medication monitoring are now ...

Pharmacovigilance is to track and detect new adverse drug reactions mainly due to drugs or due to any other chemical substance or similar entity. The knowledge of a drug’s adverse reactions can be increased by various means, including spontaneous reporting, intensive monitoring and literature searching. But, in this review, we discuss how the medical literature plays a crucial role in pharmacov...

Pharmacovigilance is the activity related to the collection, analysis and prevention of adverse drug reactions (ADRs) induced by drugs. It leads to the safety survey of pharmaceutical products. The pharmacovigilance process benefits from the traditional statistical approaches and also from the qualitative information on semantic relations between close ADR terms, such as SMQs or hierarchical le...

BACKGROUND Work on pharmacovigilance systems using texts from PubMed and Twitter typically target at different elements and use different annotation guidelines resulting in a scenario where there is no comparable set of documents from both Twitter and PubMed annotated in the same manner. OBJECTIVE This study aimed to provide a comparable corpus of texts from PubMed and Twitter that can be use...

Pharmacovigilance and methods of Signal Detection

The goal of pharmacovigilance is to detect, monitor, characterize and prevent adverse drug events (ADEs) with pharmaceutical products. This article is a comprehensive structured review of recent advances in applying natural language processing (NLP) to electronic health record (EHR) narratives for pharmacovigilance. We review methods of varying complexity and problem focus, summarize the curren...

Adverse drugs events (ADEs) detection constitutes a considerable concern in patient safety and public health care. For this reason, it is important to develop methods that improve ADE signal detection in pharmacovigilance databases. Our objective is to apply 3D pharmacophoric similarity models to enhance ADE recognition in Offsides, a pharmacovigilance resource with drug-ADE associations extrac...

The objective of this study was to apply 2 data-mining algorithms to a drug safety database to determine if these methods would have flagged potentially fatal/disabling adverse drug reactions that triggered black box warnings/drug withdrawals in advance of initial identification via "traditional" methods. Relevant drug-event combinations were identified from a journal publication. Data-mining a...

OBJECTIVES While active surveillance for adverse drug events using electronic medical records (EMRs) and claims data is expanding, these data do not fully capture patient experiences with medication-related symptoms. Our objective was to describe adherence and outcomes associated with an automated pharmacovigilance call. STUDY DESIGN/METHODS Prospective cohort of patients receiving a prescrip...