A person with type 1 diabetes drives a car in thick fog, seeing only a few meters ahead. Blood sugar can plummet or spike, and the insulin pump must react in advance. A four-hour forecast is an attempt to glimpse the road's turn through the haze.

Standard neural networks predict step by step, like a driver who recalculates the route every second based on the previous command. Errors accumulate, and after a couple of hours the forecast drifts downward — the network systematically underestimates. A physiological model relies on equations of body function, but it's an averaged portrait: it doesn't account for individual metabolism.

The new approach, PhysioSeq2Seq, works like an experienced navigator. First, over three hours of observations, it finds a digital twin in the database whose sugar levels match the patient's current data. Then it takes 10 internal variables from that twin (such as glucose in blood and tissues, and how quickly it's consumed) and feeds them to the neural network. The network then outputs a forecast for 48 steps ahead at once, without looking back at its own previous answers. It's like plotting the entire route to the destination in one go, rather than recalculating every meter.

This 'hybrid' keeps the forecast from veering off from reality: physiology holds it within reasonable bounds, while machine learning adapts to the individual. As a result, the system helps the insulin pump work proactively, as if the fog lifted and the driver could see not just the hood but the entire curve ahead.