A new technique that uses Twitter data can discover potentially dangerous drug interactions and unknown side-effects even before doctors and researchers have heard of them, scientists say.
Researchers developed a computer programme that can efficiently search millions of tweets on Twitter for the names of many drugs and medicines and build a map of how they are connected, using the hashtags that link them.
“Our new algorithm is a great way to make discoveries that can be followed-up and tested by experts like clinical researchers and pharmacists,” said Ahmed Abdeen Hamed, a computer scientist at the University of Vermont who led the creation of the new tool.
“We may not know what the interaction is, but with this approach we can quickly find clear evidence of drugs that are linked together via hashtags,” Hamed said.
The new approach could also be used to generate public alerts, Hamed said, before a clinical investigation is started or before health care providers have received updates.
“It can tell us: we may be seeing a drug/drug interaction here. Beware,” Hamed said.
Previous studies have shown that Twitter can be mined for bad drug interactions, but the Vermont team advances this idea by focusing on the distinctive information contained in hashtags – like “#overprescribed,” “#kidneystoneprobs,” and “#skinswelling” – to find new associations.
“Each individual hashtag functions almost like a neuron in the human brain, sending a specific signal,” the scientists said, that can show a surprising pathway between two or more drugs.
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, said Hamed.
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,” said Tamer Fandy, a professor of pharmaceutical sciences at the Albany College of Pharmacy’s campus in Vermont.
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 by the hashtag “#happythanksgiving.”
The study was published in the Journal of Biomedical Informatics.