  Research on decision support applications in healthcare, such as those
related to diagnosis, prediction, treatment planning, etc., have seen
enormously increased interest recently. This development is thanks to the
increase in data availability as well as advances in artificial intelligence
and machine learning research. Highly promising research examples are published
daily. However, at the same time, there are some unrealistic expectations with
regards to the requirements for reliable development and objective validation
that is needed in healthcare settings. These expectations may lead to unmet
schedules and disappointments (or non-uptake) at the end-user side. It is the
aim of this tutorial to provide practical guidance on how to assess performance
reliably and efficiently and avoid common traps. Instead of giving a list of
do's and don't s, this tutorial tries to build a better understanding behind
these do's and don't s and presents both the most relevant performance
evaluation criteria as well as how to compute them. Along the way, we will
indicate common mistakes and provide references discussing various topics more
in-depth.
