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

TL;DR

This paper introduces the Intelligent Perioperative System (IPS), a real-time big data analytics platform designed to assess surgical risks and improve postoperative complication predictions through dynamic interaction with physicians.

Contribution

It presents a novel real-time system integrating big data frameworks for dynamic surgery risk assessment and physician interaction, advancing current predictive tools.

Findings

Demonstrates feasibility of real-time risk analysis

Integrates big data frameworks for scalable processing

Provides visualization for system validation

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.