Sepsis is a life-threatening reaction to an infection, responsible for 6 million deaths globally each year. Moreover, this condition is one of the major cost to healthcare. Our aim is to develop a new technique for the early detection of the sepsis onset. Such an early detection would allow for the improvement of sepsis outcome.Our technique is based on the assumption that accurate and early prediction of sepsis requires to be able to predict the evolution of the vital signs. This idea was translated in the use of a recurrent neural network, a Long Short-term memory (LSTM) network, which was trained to accomplish two tasks: the prediction of (i) sepsis and (ii) the vital signs at time t+6. We assume that the use of this auxiliary task allows for a better training of the network given the low prevalence of sepsis. The network consists in three modules: (i) an embedding module aiming at providing a compact representation of the inputs, (ii) a recurrent module with three LSTMs layers with highway connection between each layer (iii) the prediction modules consisting in linear layers for the prediction of two tasks.The network achieved a final utility score of 0.309 on the full hidden test set (0.387 on the test set A, 0.365 on the set B , and -0.148 on the set C). The team name was "IADI".Further improvements are required before transferring such an approach into clinical practice.