# Reports: 20,346,289
Data Range: 2004q1 - 2023q3
AERSMine


AERSMine:

analyze differential clinical outcomes across
the spectrum of human diseases and drugs



AERSMine is a multi-cohort analyzing application designed to mine data across millions of patient reports (currently 20,346,289) from the FDA’s Adverse Event Reporting System.

Perform focused {patients X meds} group comparisons, high dimensional subset–based correlation analyses, view differential reporting patterns to identify high-risk demographics subgroups, and unravel latent relationships within large clinical effects data. Gather new insights on inter-correlated adverse events and population subgroups, recognize potential safety signals and generate testable hypotheses based on risk-altering interactions. Our long-term hypothesis is that by correlation of adverse reactions with known drug-phenotype-gene relationships, we will improve our ability to modify therapeutic strategies and improve therapeutic efficacy.

Citation: Sarangdhar, M. et al. Nat. Biotechnol. 34, 697–700 (2016).

Explore

Discover novel patterns across multiple population subgroups, generate analyzable data matrices, identify unexpectedly high-risk subgroups, mechanistically linked ADRs, visualize and export analyses.


AERSMine:A novel framework for FAERS data mining

AERSMine is a multi-cohort analyzing application designed to mine data from the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS, legacy AERS, LAERS). AERSMine allows comparative analysis of differential clinical outcomes and adverse events (reactions) in response to approved and/or investigational drugs (therapeutics). Differential adverse event (ADR) risk profiles can be identified via well-established disproportionality analysis methods used in pharmacovigilance including relative risk (RR), and safety signal detection methods, such as Information Component (IC) and drug-drug-interaction (DDI) metric Omega. In addition, AERSMine can facilitate pharmacology-based novel discoveries via identification of drug repositioning (repurposing) candidates to improve treatment strategies. With AERSMine researchers can gather new insights on inter-correlated ADRs and population subgroups, recognize potential safety signals and generate testable hypotheses based on risk-altering interactions. AERSMine uses the Anatomical Therapeutic Chemical Classification System (ATC-KEGG) and MedDRA, Medical Dictionary for Regulatory Activities (MedDRA) ontologies for drugs, clinical indications and ADR aggregation respectively. AERSMine was recently published in Nature Biotechnology (NatBiol, Natbio, NBT).