  In this position paper we describe a general framework for applying machine
learning and pattern recognition techniques in healthcare. In particular, we
are interested in providing an automated tool for monitoring and incrementing
the level of awareness in the operating room and for identifying human errors
which occur during the laparoscopy surgical operation. The framework that we
present is divided in three different layers: each layer implements algorithms
which have an increasing level of complexity and which perform functionality
with an higher degree of abstraction. In the first layer, raw data collected
from sensors in the operating room during surgical operation, they are
pre-processed and aggregated. The results of this initial phase are transferred
to a second layer, which implements pattern recognition techniques and extract
relevant features from the data. Finally, in the last layer, expert systems are
employed to take high level decisions, which represent the final output of the
system.
