Dataset Open Access

Drug Indications Extracted from FAERS

Stupp, Gregory S; Su, Andrew I


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    "description": "<p>This dataset contains drug indications extracted from the&nbsp;FDA Adverse Event Reporting System (<a href=\"https://www.fda.gov/drugs/guidancecomplianceregulatoryinformation/surveillance/adversedrugeffects/\">FAERS</a>).</p>\n\n<p>Source code here:&nbsp;<a href=\"https://github.com/stuppie/faers\">https://github.com/stuppie/faers</a></p>\n\n<p><strong>Method Outline</strong></p>\n\n<ul>\n\t<li>Data files are extracted from zip files, parsed from csvs, and imported into a MySQL database (see&nbsp;parser.py).</li>\n\t<li>Duplicate records are then de-duplicated by taking only the most recent version for each case ID (see&nbsp;dedupe.py).</li>\n\t<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&nbsp;normalize_indications.py)</li>\n\t<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&#39;t match are run against rxnorm&#39;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&nbsp;67/100. The matched rxnorm CUIs are then mapped to the their Ingredient level rxnorm ID. (See&nbsp;normalize_drugs.py)</li>\n\t<li>Indications are then retrieved for each drug ingredient and filtered to require a minimum of 20 individual occurances. (See&nbsp;get_indications.py)</li>\n</ul>"
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