Causing trouble

摘要

IN THE mid-1990s, an algorithm trained on hospital admission data made a surprising prediction. It said that people who presented with pneumonia were more likely to survive if they also had asthma. This flew in the face of all medical knowledge, which said that asthmatic patients were at increased risk from the disease. Yet the data gathered from multiple hospitals was indisputable: if you had asthma, your chances were better. What was going on? It turned out that the algorithm had missed a crucial piece of the puzzle. Doctors treating pneumonia patients with asthma were passing them straight to the intensive care unit, where the aggressive treatment significantly reduced their risk of dying from pneumonia. It was a case of cause and effect being hopelessly entangled. Fortunately, no changes were rolled out on the basis of the algorithm.