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2 results for “FAERS”
Drug Indications Extracted from FAERS
<p>This dataset contains drug indications extracted from the FDA Adverse Event Reporting System (<a href="https://www.fda.gov/drugs/guidancecomplianceregulatoryinformation/surveillance/adversedrugeffects/">FAERS</a>).</p> <p>Source code here: <a href="https://github.com/stuppie/faers">https://github.com/stuppie/faers</a></p> <p><strong>Method Outline</strong></p> <ul> <li>Data files are extracted from zip files, parsed from csvs, and imported into a MySQL database (see parser.py).</li> <li>Duplicate records are then de-duplicated by taking only the most recent version for each case ID (see dedupe.py).</li> <li>Indications are normalized by matching to UMLS terms by string matching. Cross-references to Human Phenotype Ontology are pulled from UMLS and xrefs to Monarch Disease Ontology (MONDO) are pulled from MONDO using the UMLS xrefs. (See normalize_indications.py)</li> <li>Drugs names are normalized first by applying a few simple string cleaning operations (strip, fix slashes and periods). Then they are attempted to be matched to rxnorm by exact string matching. Those that don't match are run against rxnorm's <a href="https://rxnav.nlm.nih.gov/RxNormAPIs.html#uLink=RxNorm_REST_getApproximateMatch">approximate matching service</a>, and are accepted if the score is higher than 67/100. The matched rxnorm CUIs are then mapped to the their Ingredient level rxnorm ID. (See normalize_drugs.py)</li> <li>Indications are then retrieved for each drug ingredient and filtered to require a minimum of 20 individual occurances. (See get_indications.py)</li> </ul>
US FDA FAERS Drug Adverse Event Dataset Processed as Relational CSV
<p>WARNING: This is an early attempt at processing a large dataset, and I have not yet completely validated the processing scripts or the resulting CSV files.</p> <p>This is a compressed archive of the US FDA FAERS Drug Averse Event database, obtained from open.fda.gov (https://open.fda.gov) and processed with scripts that I wrote (https://github.com/dbuijs/faers2csv) to convert the 100 GB of JSON files into relational CSV that take up approximately 6 GB of space and are suitable for loading into a relational database such as PostgreSQL. </p> <p>This version of the data was processed on January 29, 2016. </p> <p>faerscsv.tar.bz2 expands into a folder called csv, with 4 relational CSV files for each source JSON file.</p> <p>faerstats.tar.bz2 expands into a folder called stats, with a text file for each CSV file containing summary statistics generated by csvstat (Python CSVkit).</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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OpenNeuro
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