  Workplace injuries result in substantial human and financial losses. As
reported by the International Labour Organization (ILO), there are more than
374 million work-related injuries reported every year. In this study, we
investigate the problem of injury risk prediction and prevention in a work
environment. While injuries represent a significant number across all
organizations, they are rare events within a single organization. Hence,
collecting a sufficiently large dataset from a single organization is extremely
difficult. In addition, the collected datasets are often highly imbalanced
which increases the problem difficulty. Finally, risk predictions need to
provide additional context for injuries to be prevented. We propose and
evaluate the following for a complete solution: 1) several ensemble-based
resampling methods to address the class imbalance issues, 2) a novel transfer
learning approach to transfer the knowledge across organizations, and 3)
various techniques to uncover the association and causal effect of different
variables on injury risk, while controlling for relevant confounding factors.
