Prognostics of surgical site infections using dynamic health data.
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.
Department of Computer Science, University of Rochester, United States.
Chuyang
C
Ke
Department of Industrial & Systems Engineering, University of Washington, United States.
Yan
Y
Jin
Department of Surgery, University of Washington, United States.
Heather
H
Evans
Department of Biomedical Informatics and Medical Education, University of Washington, United States.
Bill
B
Lober
Department of Electrical & Computer Engineering, Texas A&M University, United States.
Xiaoning
X
Qian
Department of Computer Science, University of Rochester, United States.
Ji
J
Liu
Department of Industrial & Systems Engineering, University of Washington, United States. Electronic address: shuai.huang.ie@gmail.com.
Shuai
S
Huang
D000465:Algorithms; D005544:Forecasting; D006801:Humans; D011379:Prognosis; D012307:Risk Factors; D013530:Surgical Wound Infection; D017216:Telemedicine / Q000706:statistics & numerical data*
Machine learning; Risk prediction; Surgical Site Infections (SSI); mHealth