  Predicting critical health outcomes such as patient mortality and hospital
readmission is essential for improving survivability. However, healthcare
datasets have many concurrences that create complexities, leading to poor
predictions. Consequently, pre-processing the data is crucial to improve its
quality. In this study, we use an existing pre-processing algorithm,
concatenation, to improve data quality by decreasing the complexity of
datasets. Sixteen healthcare datasets were extracted from two databases - MIMIC
III and University of Illinois Hospital - converted to the event logs, they
were then fed into the concatenation algorithm. The pre-processed event logs
were then fed to the Split Miner (SM) algorithm to produce a process model.
Process model quality was evaluated before and after concatenation using the
following metrics: fitness, precision, F-Measure, and complexity. The
pre-processed event logs were also used as inputs to the Decay Replay Mining
(DREAM) algorithm to predict critical outcomes. We compared predicted results
before and after applying the concatenation algorithm using Area Under the
Curve (AUC) and Confidence Intervals (CI). Results indicated that the
concatenation algorithm improved the quality of the process models and
predictions of the critical health outcomes.
