A Preliminary Approach for Learning Relational Policies for the
  Management of Critically Ill Children
  The increased use of electronic health records has made possible the
automated extraction of medical policies from patient records to aid in the
development of clinical decision support systems. We adapted a boosted
Statistical Relational Learning (SRL) framework to learn probabilistic rules
from clinical hospital records for the management of physiologic parameters of
children with severe cardiac or respiratory failure who were managed with
extracorporeal membrane oxygenation. In this preliminary study, the results
were promising. In particular, the algorithm returned logic rules for medical
actions that are consistent with medical reasoning.
