  Surgical Site Infection (SSI) is a national priority in healthcare research.
Much research attention has been attracted to develop better SSI risk
prediction models. However, most of the existing SSI risk prediction models are
built on static risk factors such as comorbidities and operative factors. In
this paper, we investigate the use of the dynamic wound data for SSI risk
prediction. There have been emerging mobile health (mHealth) tools that can
closely monitor the patients and generate continuous measurements of many
wound-related variables and other evolving clinical variables. Since existing
prediction models of SSI have quite limited capacity to utilize the evolving
clinical data, we develop the corresponding solution to equip these mHealth
tools with decision-making capabilities for SSI prediction with a seamless
assembly of several machine learning models to tackle the analytic challenges
arising from the spatial-temporal data. The basic idea is to exploit the
low-rank property of the spatial-temporal data via the bilinear formulation,
and further enhance it with automatic missing data imputation by the matrix
completion technique. We derive efficient optimization algorithms to implement
these models and demonstrate the superior performances of our new predictive
model on a real-world dataset of SSI, compared to a range of state-of-the-art
methods.
