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arXiv · 1709.10192

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

Zheng Feng·Rajendra Rana Bhat·Xiaoyong Yuan·Daniel Freeman·Tezcan Baslanti·Azra Bihorac·Xiaolin Li
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Abstract

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.

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Zheng Feng, Rajendra Rana Bhat, Xiaoyong Yuan, Daniel Freeman, Tezcan Baslanti, Azra Bihorac, Xiaolin Li. 2017-09-28. Intelligent Perioperative System: Towards Real-time Big Data Analytics in Surgery Risk Assessment. https://arxiv.org/abs/1709.10192

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