Machine-learning algorithm identifies sex-specific risks of adverse drug effects

Written by Ilana Landau, Editor

In a proof-of-concept study, investigators from Columbia University (NY, USA) have described a machine-learning algorithm that identifies and could help predict adverse drug effects that pose increased risks to men or women, which could ultimately impact prescribing and dosage patterns to help prevent adverse drug effects. Historically, trials and studies of new drugs have been conducted in relatively homogenous patient populations, with over-representation of men. This can bias the data and impact the generalizability of results with respect to both drug effectiveness and safety in wider patient populations. Worryingly, adverse drug reactions represent the fourth leading cause of mortality in...

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