There is a paucity of real-world clinical data that evaluates adverse drug effects in women, among other under-served populations, due to a long history of trials done on relatively homogenous patient populations (healthy white males). Without heterogeneous data availability, biased results leave women in the dangerous position of not having accurate information on adverse drug effects, currently the fourth-leading cause of death in the U.S.
An example of this issue is Ambien, an insomnia drug that previously had the same dosage prescribed for both men and women. When evidence appeared that women were having a significantly greater rate of adverse reactions the following morning, the FDA reduced the recommended dosage by half in 2013.
“Rather than take the stance that we wait for evidence to become so overwhelming that we have to do something about, we wanted to be more proactive,” said Nicholas Tatonetti, PhD, a Columbia University researcher who co-led a study in Patterns that uses machine learning to identify these adverse effects in women. “We want to use databases like the Adverse Event Reporting System (FAERS) from the FDA or the electronic health records to get a jump on identifying sex-specific adverse events before it’s too late.”
Tatonetti, an associate professor in the Department of Biomedical Informatics, collaborated with Payal Chandak, Columbia PhD student in the Department of Computer Science, to develop AwareDX (Analysing Women At Risk for Experiencing Drug toxicity), an algorithm that leverages advances in machine learning to predict sex risks.
They discuss the study in this video.
