Figure 1:
Illustration of data processing pipeline. (1) We extract vital signs and lab results (xn) are extracted from the database for a filtered selection of patients. (2) A switching-state autoregressive model is used the model the time series, generating belief states bn (the probability of each state at each time). (3) Static features are extracted for all patients (sn) - these are based on admission data and do not change over the course of the subject’s stay. (4) Given three possible sets of features for each timestep t and patient n - sn, xnt, and bnt - we train a classifier to predict the per-timestep outcome of interest ynt (e.g. vasopressor administration). Our system predicts the outcome ynt using features from either the immediately previous timestep fn,(t–1), or some further delay fn,(t–d).