  Adherence can be defined as "the extent to which patients take their
medications as prescribed by their healthcare providers"[Osterberg and
Blaschke, 2005]. World Health Organization's reports point out that, in
developed countries, only about 50% of patients with chronic diseases correctly
follow their treatments. This severely compromises the efficiency of long-term
therapy and increases the cost of health services. We propose in this paper
different models of patient drug consumption in breast cancer treatments. The
aim of these different approaches is to predict medication non-adherence while
giving insights to doctors of the underlying reasons of these illegitimate
drop-outs. Working with oncologists, we show the interest of Machine- Learning
algorithms fined tune by the feedback of experts to estimate a risk score of a
patient's non-adherence and thus improve support throughout their care path.
