[h=1]Data mining for deadly drug combinations[/h]
Sam Roe and
Karisa KingContact Reporters Chicago Tribune
February 11, 2016
The experiment began with thousands of patient files, millions of prescription orders, billions of clinical measurements and a single question: Could big data be used to discover deadly drug combinations?
For decades, scientists have wrestled with the problem that trusted prescription medications can combine in dangerous ways, often placing Americans at risk when they take more than one drug. Sometimes the dangers are well-documented. In other cases, they remain hidden from everyone: doctors, pharmacists, drugmakers and patients.
But in recent years a new era of data analysis has dawned. Across fields as varied as medicine and finance, researchers are developing powerful new mathematical formulas to reveal patterns within patterns and clues within clues.
The Chicago Tribune, in a
unique collaboration with data scientists, pharmacologists and cellular researchers at Columbia University Medical Center, set out to see if these same novel techniques could be used in the hunt for risky drug pairs.
Two years later, the results are in: The team has identified four drug combinations associated with a heart condition that can lead to a potentially fatal arrhythmia. The drugs include several widely prescribed medications, none of them linked to the cardiac condition on its own...
