FDA Drug Adverse Events Reporting System FAERS 2021

FAERS data package is designed to support the FDA's post-marketing safety surveillance program for drug and therapeutic biologic products. It is a useful tool for activities such as looking for new safety concerns that might be related to a marketed product, evaluating a manufacturer's compliance with reporting regulations and responding to outside requests for information. The reports in FAERS are evaluated by clinical reviewers, in the Center for Drug Evaluation and Research (CDER) and the Center for Biologics Evaluation and Research (CBER), to monitor the safety of products after they are approved by FDA. These reports are submitted to FDA by healthcare professionals (such as physicians, pharmacists, nurses and others) and consumers (such as patients, family members, lawyers and others). Healthcare professionals and consumers may also report to the products’ manufacturers who then send these reports to the FDA. The datasets in FAERS data package contain much relevant information such as patient demographics, drug name, dose and route of administration, diagnosis and indication for use, therapy starts and ends date and patient outcome.

The use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the Data Library  on AWS. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes.

1. FDA Adverse Events Reporting System Demographics 2021

The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to the FDA (U.S. Food and Drug Administration) in 2021. The Demographics dataset contains patient demographic and administrative information, a single record for each event report.

2. FDA Adverse Events Reporting System Drug 2021

The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2021. The Drug dataset contains drug/biologic information for as many medications as were reported for the event (1 or more per event).

3. FDA Adverse Events Reporting System Drug Indication 2021

The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to the FDA (U.S. Food and Drug Administration) in 2021. The Indication dataset contains all "Medical Dictionary for Regulatory Activities" (MedDRA) terms coded for the indications for use (diagnoses) for the reported drugs (0 or more per drug per event).

4. FDA Adverse Events Reporting System Drug Reaction 2021

The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2021. The drug reaction dataset contains all "Medical Dictionary for Regulatory Activities" (MedDRA) terms coded for the adverse event (1 or more).

5. FDA Adverse Events Reporting System Drug Therapy Dates 2021

The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2021. The drug therapy dates dataset contains drug therapy start dates and end dates for the reported drugs.

6. FDA Adverse Events Reporting System Patient Outcome 2021

The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2021. The patient outcome file contains information on patient outcomes for the event (0 or more).

7. FDA Adverse Events Reporting System Report Source 2021

The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2021. The report source dataset contains report sources for event.

We deliver high-quality data

• Each dataset goes through 3 levels of quality review
  • 2 Manual reviews are done by domain experts
  • Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints
• Data is normalized into one unified type system
  • All dates, unites, codes, currencies look the same
  • All null values are normalized to the same value
  • All dataset and field names are SQL and Hive compliant
• Data and Metadata
  • Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters
  • Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated
• Data Updates
  • Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted

Our data is curated and enriched by domain experts

Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:

• Field names, descriptions, and normalized values are chosen by people who actually understand their meaning
• Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset
• Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations
• The data is always kept up to date – even when the source requires manual effort to get updates
• Support for data subscribers is provided directly by the domain experts who curated the data sets
• Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.

• If you have questions about our products, contact us at info@johnsnowlabs.com .

John Snow Labs , an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects.