A few years ago, Adam Chekroud tried something new. The Yale University neuroscientist pulled as much data as he could out of questionnaires that had been given to people in a large clinical trial of drugs used to treat depression. The data included obvious elements such as the study subjects' race, gender, and education level, but it went much deeper, too. It included the answers that study participants had given to questions about themselves. For example, had they been bothered by aches and pains throughout their body? Did standing in long lines make them fearful and anxious?
Then Chekroud and his colleagues had a machine-learning algorithm look at how these various factors correlated with the patients' responses to a common depression drug. It turned out that a combination of 25 factorsâ??—â??including the answers to questions like that one about long linesâ??—â??predicted whether patients would be helped by the treatment or not.
How your reaction to queuing, combined with 24 other apparently unrelated factors, might help explain why a particular drug does or does not improve your brain chemistry is a total mystery. Why those factors and not 25 others? Why lines and not, say, enclosed spaces? But no matter. It's a clue.
