  Background: The concept of combinatorial biomarkers was conceived around
2010: it was noticed that simple biomarkers are often inadequate for
recognizing and characterizing complex diseases.
  Methods: Here we present an algorithmic search method for complex biomarkers
which may predict or indicate Alzheimer's disease (AD) and other kinds of
dementia. We applied data mining techniques that are capable to uncover
implication-like logical schemes with detailed quality scoring. Our program
SCARF is capable of finding multi-factor relevant association rules
automatically. The new SCARF program was applied for the Tucson, Arizona based
Critical Path Institute's CAMD database, containing laboratory and cognitive
test data for more than 6000 patients from the placebo arm of clinical trials
of large pharmaceutical companies, and consequently, the data is much more
reliable than numerous other databases for dementia.
  Results: The results suggest connections between liver enzyme-, B12 vitamin-,
sodium- and cholesterol levels and dementia, and also some hematologic
parameter-levels and dementia.
