Demo: http://ec2-54-67-36-107.us-west-1.compute.amazonaws.com:8888/static/index.html#/
Team: scikit_hammertime
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Jake Beard: jake at minnow dot io
Anjney Midha: anjney at stanford dot edu
Ankit Kumar: ankitk at stanford dot edu
Jay Hack: jhack at stanford dot edu
Ross Lazerowitz: rosslazer at gmail dot com
Bayes Impact Hackathon 2014
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The Problem:
- 100,000 Americans die each year due to known drug side effect
- Existing tools for users to search for adverse drug interactions are clunky, database level query interfaces
- Existing tools are limited to reported drug events - which are severely prone to underreporting
Solution:
- We use a distributed representation of the AERS ( Federal Drug Adverse Event Reporting System) dataset classified by the RxNorm hierarchy, using neural networks to predict novel interactions for pairs of drugs that do not have a historical interaction record
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acknowledgements:
Libraries used:
- Word2vec (https://code.google.com/p/word2vec/)
Papers Referenced: