Fig. 3.

a Week-long day-by-day performances of our logistic regression (LR) and three-layer multilayer perceptron (3-MLP) model. As shown in the figures, our models' performances rise daily from admission to the end of the first week. Daily accuracy for mortality prediction are: [Log-Reg, 3MLP]: Day 0—[58.9%, 61.9%], Day 1—[63.2%, 63.3%], Day 2—[65.3%, 66.7%], Day 3—[70%, 69.7%], Day 4—[70.3%, 71.9%], Day 5—[71.7%, 72.8%], Day 6—[74.1%, 73.6%]. b The importance of clinical parameters for the week-long mortality prediction model changes day-by-day. We are showing here the evolution of the SHAP (SHapley Additive exPlanations) importance of the clinical variables from admission (Day 1–2) to end of the first week, particularly PaO₂/FiO₂, higher peak pressure, higher ventilatory ratio, lower pH, higher lactate, lower platelet count, higher CRP, lower oxygen saturations, higher PEEP and higher PaCO₂. Note, how some of the variables are systematically high importance or low importance while others are systematically increasing from day 1 and vice versa, see Fig. S4 for details