Feature Selection for Bayesian Evaluation of Trauma Death Risk
  In the last year more than 70,000 people have been brought to the UK
hospitals with serious injuries. Each time a clinician has to urgently take a
patient through a screening procedure to make a reliable decision on the trauma
treatment. Typically, such procedure comprises around 20 tests; however the
condition of a trauma patient remains very difficult to be tested properly.
What happens if these tests are ambiguously interpreted, and information about
the severity of the injury will come misleading? The mistake in a decision can
be fatal: using a mild treatment can put a patient at risk of dying from
posttraumatic shock, while using an overtreatment can also cause death. How can
we reduce the risk of the death caused by unreliable decisions? It has been
shown that probabilistic reasoning, based on the Bayesian methodology of
averaging over decision models, allows clinicians to evaluate the uncertainty
in decision making. Based on this methodology, in this paper we aim at
selecting the most important screening tests, keeping a high performance. We
assume that the probabilistic reasoning within the Bayesian methodology allows
us to discover new relationships between the screening tests and uncertainty in
decisions. In practice, selection of the most informative tests can also reduce
the cost of a screening procedure in trauma care centers. In our experiments we
use the UK Trauma data to compare the efficiency of the proposed technique in
terms of the performance. We also compare the uncertainty in decisions in terms
of entropy.
