Data-driven visualization of drug interactions. Adverse Drug Events Almost 1 million deaths/injuries each year in the US [1] Some fraction of ADEs are.

$Page 1: Data-driven visualization of drug interactions. Adverse Drug Events Almost 1 million deaths/injuries each year in the US [1] Some fraction of ADEs are.$
Data-driven visualization of drug interactions

Adverse Drug Events

• Almost 1 million deaths/injuries each year in the US[1]

• Some fraction of ADEs are caused by previously unknown drug-drug interactions• Clinical trials aren’t large enough to detect many potential

interactions

• FDA, WHO, pharmaceutical companies maintain databases of reported[2] ADEs• You can download a sample of the FDA data from the Adverse

Event Reporting System website[3]

• We can analyze the reported data to identify suspicious drug interactions

Copyright 2011 Cloudera Inc. All rights reserved

Challenges in Analyzing Adverse Drug Events

• Biased Sample• Adverse event reporting is voluntary• We don’t see events from patients who took the drugs and

nothing happened

• Correlation != Causation• No controlled trials, some correlations are coincidences

• Requires Advanced Statistical Modeling Skills• Multi-item Gamma Poisson Shrinkage Estimator is used to

score the significance of a drug interactions• The model is too complex to solve directly, we use Expectation

Maximization (EM) to estimate its parameters


The Hard Problem: Counting

• It is a “small” data problem…• 250,000+ events reported to the FDA annually

• …that explodes when we consider:• Multi-drug, multi-symptom interactions• Analyzed by strata (e.g., month of report, patient age, patient

gender, etc.)• ~1 million reports => ~360 million buckets

• Analysts typically filter the data to only consider a few adverse reactions at a time…

• …but that is not the way of the data scientist


Solving the Hard Problem

• MapReduce on Hadoop• 20 MapReduce jobs• Filter, aggregate, join, aggregate again• Model the resulting data in R• Use MapReduce to apply the model parameters to the data,

score each drug-drug interaction, and then filter the data to obtain the highest scoring interactions

• Visualizing the Results• Even applying a restrictive filter on the scores, we end up with

20,000+ statistically significant drug-drug-reaction triples


The Drug-Drug Interaction Graph


HIV Medications


Cancer Medications


Exploring the Graph


Bridges Between Dense Clusters



Acknowledgments and References

• Thanks to Josh Wills, Director of Data Science at Cloudera, for the data collection and analysis shown here.

• References:• [1] ADE instances/year:

http://www.ahrq.gov/qual/aderia/aderia.htm• [2] AERS reporting site:

http://www.ahrq.gov/qual/aderia/aderia.htm• [3] Download ADE instance data:

http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Surveillance/AdverseDrugEffects/ucm082193.htm

• Other resources:• http://www.cloudera.com/blog• http://wiki.cloudera.com/

Copyright 2011 Cloudera Inc. All rights reserved 12

http://www.ahrq.gov/qual/aderia/aderia.htm

Data-driven visualization of drug interactions. Adverse Drug Events Almost 1 million deaths/injuries each year in the US [1] Some fraction of ADEs are.

Documents