Intelligent Perioperative System: Towards Real-time Big Data Analytics in Surgery Risk Assessment
This addresses the problem for physicians needing better tools to manage patient treatment by providing an incremental real-time analytics platform for surgery risk assessment.
The paper tackles the lack of comprehensive real-time platforms for surgery risk assessment by proposing the Intelligent Perioperative System (IPS), which dynamically analyzes postoperative complication risks from large-scale patient data and interacts with physicians, demonstrating feasibility through a prototype and visualization.
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.