IEEE J Transl Eng Health Med. 2015 Oct 1;3:3000109. doi: 10.1109/JTEHM.2015.2485268. eCollection 2015.
The effective use of data within intensive care units (ICUs) has great potential to create new cloud-based health analytics solutions for disease prevention or earlier condition onset detection. The Artemis project aims to achieve the above goals in the area of neonatal ICUs (NICU). In this paper, we proposed an analytical model for the Artemis cloud project which will be deployed at McMaster Children's Hospital in Hamilton. We collect not only physiological data but also the infusion pumps data that are attached to NICU beds. Using the proposed analytical model, we predict the amount of storage, memory, and computation power required for the system. Capacity planning and tradeoff analysis would be more accurate and systematic by applying the proposed analytical model in this paper. Numerical results are obtained using real inputs acquired from McMaster Children's Hospital and a pilot deployment of the system at The Hospital for Sick Children (SickKids) in Toronto.
重症监护病房(ICU)内有效利用数据,极有可能为疾病预防或更早发现疾病提供新的基于云的健康分析解决方案。Artemis 项目旨在新的生儿重症监护病房(NICU)领域实现上述目标。在本文中,我们为将在汉密尔顿麦克马斯特儿童医院部署的 Artemis 云项目提出了一个分析模型。我们不仅收集生理数据,还收集附在 NICU 病床的输液泵数据。使用所提出的分析模型,我们预测系统所需的存储量、内存和计算能力。通过在本文中应用所提出的分析模型,容量规划和权衡分析将更加准确和系统。使用从麦克马斯特儿童医院获得的实际输入和在多伦多 SickKids 医院的系统试点部署获得了数值结果。