Prognostics of Surgical Site Infections using Dynamic Health Data

TL;DR

This paper introduces a novel machine learning framework that leverages dynamic, spatial-temporal wound data and matrix completion techniques to improve surgical site infection risk prediction, surpassing existing static models.

Contribution

It develops an innovative predictive model that integrates low-rank spatial-temporal data exploitation and automatic missing data imputation for SSI risk assessment.

Findings

Superior performance on real-world SSI dataset

Effective handling of missing data in dynamic clinical data

Enhanced prediction accuracy over state-of-the-art methods

Abstract

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.