Twitter: Early Warning System For Dangerous Drug Interactions

Twitter: Early Warning System For Bad Drug Interactions
A team of scientists has invented a new technique for discovering potentially dangerous drug interactions and unknown side-effects - before they show up in medical databases, like PubMed, or even before doctors and researchers have heard of them at all.
The far-seeing tool? A computer program that can efficiently search millions of tweets on Twitter for the names of many drugs and medicines - and build a map of how they're connected, using the #hashtags that link them. A report on how the algorithm works, and its preliminary discoveries, was published in the Journal of Biomedical Informatics.
Previous studies have shown that Twitter can be mined for bad drug interactions, but the new study advances this idea by focusing on the distinctive information contained in hashtags - like "#overprescribed", "#kidneystoneprobs", and "#skinswelling" - to find new associations.
The team's approach involves building what they call a "K-H network" - essentially a dense map of links between keywords and hashtags - and then pruning out a lot of the "noise and trash", to find the terms that are central to the network. Then the algorithm, called HashPairMiner, searches this cleaned-up network for the shortest paths between a pair of search terms and their intervening hashtags.
The overall goal of the project is to "discover any relationship between two drugs that is not known," according to the researchers. But to ground-truth the hypothesis - that data-mining in Twitter can find unknown drug interactions - the team wanted to demonstrate that their approach can produce interactions that are already known. In one example from the new study, a path between aspirin and the allergy medication benadryl, that are known to interact, was detected by the algorithm; in one instance, the two drugs were linked - perhaps not too surprisingly - by the hashtag "#happythanksgiving".
The new system began with what the researchers initially thought was as error in November of 2013. An earlier version of the current algorithm discovered that ibuprofen and medical marijuana - which were linked by a hashtag called #Alzheimer's.
The researchers repeated the experiments, gathered different tweet data sets, but got the same result. But they couldn't find any support for the association on PubMed or other databases of clinical literature. In fact, the only study they could find, from 1989, suggested the opposite, that there was no interaction between ibuprofen and marijuana.
It turned out that the researchers had discovered people on Twitter who were sharing the results of a brand-new peer-reviewed study suggesting that ibuprofen has some ability to block or reduce the memory-damaging effects of regular marijuana use, which has been associated with the development of Alzheimer's disease.
As more states legalize marijuana, there may be increasing discussion of its interactions with other drugs - ahead of researchers capacity to study these interactions. This new algorithm are able to detect concerns, that may lead researchers to a hypothesis that can be followed up by a clinical trial or other medical test.

Based on material originally posted by University of Vermont.
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