A doctor holds a stack of test results taken at different times. Each sheet on its own is just a snapshot of a single day, but what you want is a whole movie of how the disease unfolded. A neural network called Tempo learns to glue these scattered frames into a smooth reel.

Standard event-based models (EBMs) are like an archaeologist who can only say that a pot is older than a coin, but not by how many centuries. Tempo works differently. It is trained on tens of thousands of synthetic patient stories where the order and speed at which deviations appear in biomarkers (measurable signs of disease) are known in advance. Like a child learning to walk, falling at first, the network develops a feel for typical disease trajectories.

On synthetic tests, Tempo made half as many errors in event ordering as previous leaders, and in cases with dozens of biomarkers, almost three times fewer. When fed real data from patients with Alzheimer's disease (the ADNI project), it built the following chain: first the temporal lobes shrink, then amyloid, a sticky protein that clogs the space between cells, accumulates, and only then does memory noticeably weaken. At later stages, another protein, tau, appears, and brain destruction accelerates sharply. This sequence matches what doctors already knew, but now it was obtained without a single manual hint.

Previously, for each new hypothesis about the course of a disease, scientists had to derive complex formulas. Tempo, by contrast, is like a universal camera that doesn't care what it's shooting: faces, landscapes, or microbes. It quickly runs through different assumptions and shows which one fits the data best. The stack of test results in the doctor's hands finally turns into a real movie.