  Lack of training data hinders automatic recognition and prediction of
surgical activities necessary for situation-aware operating rooms. We propose
using knowledge transfer to compensate for data deficit and improve prediction.
We used two approaches to extract and transfer surgical process knowledge.
First, we encoded semantic information about surgical terms using word
embedding which boosted learning process. Secondly, we passed knowledge between
different clinical datasets of neurosurgical procedures using transfer
learning. Transfer learning was shown to be more effective than a simple
combination of data, especially for less similar procedures. The combination of
two methods provided 22% improvement of activity prediction. We also made
several pertinent observations about surgical practices.

Machine Learning