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....
| Publicado en: | Journal of Biomedical Informatics Vol. 65; pp. 22 - 34 |
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| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Academic Press Inc.
Jan2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=121134658&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 121134658 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Jan2017 vid: 65 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 121134658 121134658 NLM27825798 121134658 10.1016/j.jbi.2016.10.021 NLM27825798 121134658 ppf: 22 ppct: 12 formats: tig: atl: Prognostics of surgical site infections using dynamic health data. aug: au: Ke, Chuyang Jin, Yan Evans, Heather Lober, Bill Qian, Xiaoning Liu, Ji Huang, Shuai affil: Department of Computer Science, University of Rochester, United States sug: subj: Surgical Wound Infection Algorithms Telemedicine Statistics and Numerical Data Prognosis Risk Factors Forecasting Human ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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