  Machine learning for early syndrome diagnosis aims to solve the intricate
task of predicting a ground truth label that most often is the outcome (effect)
of a medical consensus definition applied to observed clinical measurements
(causes), given clinical measurements observed several hours before. Instead of
focusing on the prediction of the future effect, we propose to directly predict
the causes via time series forecasting (TSF) of clinical variables and
determine the effect by applying the gold standard consensus definition to the
forecasted values. This method has the invaluable advantage of being
straightforwardly interpretable to clinical practitioners, and because model
training does not rely on a particular label anymore, the forecasted data can
be used to predict any consensus-based label. We exemplify our method by means
of long-term TSF with Transformer models, with a focus on accurate prediction
of sparse clinical variables involved in the SOFA-based Sepsis-3 definition and
the new Simplified Acute Physiology Score (SAPS-II) definition. Our experiments
are conducted on two datasets and show that contrary to recent proposals which
advocate set function encoders for time series and direct multi-step decoders,
best results are achieved by a combination of standard dense encoders with
iterative multi-step decoders. The key for success of iterative multi-step
decoding can be attributed to its ability to capture cross-variate dependencies
and to a student forcing training strategy that teaches the model to rely on
its own previous time step predictions for the next time step prediction.
