Intelligent Perioperative System: Towards Real-time Big Data Analytics in Surgery Risk Assessment

被引：9

作者：

Feng, Zheng ^{[1
]}

Bhat, Rajendra Rana ^{[1
]}

Yuan, Xiaoyong ^{[1
]}

Freeman, Daniel ^{[1
]}

Baslanti, Tezcan ^{[1
]}

Bihorac, Azra ^{[1
]}

Li, Xiaolin ^{[1
]}

机构：

[1] Univ Florida, Natl Sci Fdn, Ctr Big Learning, Gainesville, FL 32603 USA

来源：

2017 IEEE 15TH INTL CONF ON DEPENDABLE, AUTONOMIC AND SECURE COMPUTING, 15TH INTL CONF ON PERVASIVE INTELLIGENCE AND COMPUTING, 3RD INTL CONF ON BIG DATA INTELLIGENCE AND COMPUTING AND CYBER SCIENCE AND TECHNOLOGY CONGRESS(DASC/PICOM/DATACOM/CYBERSCI | 2017年

基金：

美国国家卫生研究院; 美国国家科学基金会;

关键词：

Perioprative risk prediction; Real-time processing; Big data analysis; Precision medicine; BAYESIAN NETWORK; PREDICTION;

D O I：

10.1109/DASC-PICom-DataCom-CyberSciTec.2017.201

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Surgery risk assessment is an effective tool for physicians to manage the treatment of patients, but most current research projects fall short in providing a comprehensive platform to evaluate the patients' surgery risk in terms of different complications. The recent evolution of big data analysis techniques makes it possible to develop a real-time platform to dynamically analyze the surgery risk from large-scale patients information. In this paper, we propose the Intelligent Perioperative System (IPS), a real-time system that assesses the risk of postoperative complications (PC) and dynamically interacts with physicians to improve the predictive results. In order to process large volume patients data in real-time, we design the system by integrating several big data computing and storage frameworks with the high through-output streaming data processing components. We also implement a system prototype along with the visualization results to show the feasibility of system design.

引用

页码：1254 / 1259

页数：6

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