  This thesis conducts an observational study into whether diuretics should be
administered to ICU patients with sepsis when length of stay in the ICU and
30-day post-hospital mortality are considered. The central contribution of the
thesis is a stepwise, reusable software-based approach for examining the
outcome of treatment vs no-treatment decisions with observational data. The
thesis implements, demonstrates and draws findings via three steps: Step 1.
Form a study group and prepare modeling variables. Step 2. Model the propensity
of the study group with respect to the administration of diuretics with a
propensity score function and create groups of patients balanced in this
propensity. Step 3. Statistically model each outcome with study variables to
decide whether the administration of diuretics has a significant impact.
Additionally, the thesis presents a preliminary machine learning based method
using Genetic Programming to predict mortality and length of stay in ICU
outcomes for the study group. The thesis finds, for its study group, in three
of five propensity balanced quintiles, a statistically significant longer
length of stay when diuretics are administered. For a less sick subset of
patients (SAPS ICU admission score < 17) the administration of diuretics has a
significant negative effect on mortality.
