Real-Time Analysis for Intensive Care: Development and Deployment of the Artemis Analytic System

Abstract

The lives of many thousands of children born premature or ill at term around the world have been saved by those who work within neonatal intensive care units (NICUs). Modern-day neonatologists, together with nursing staff and other specialists within this domain, enjoy modern technologies for activities such as financial transactions, online purchasing, music, and video on demand. Yet, when they move into their workspace, in many cases, they are supported by nearly the same technology they used 20 years ago. Medical devices provide visual displays of vital signs through physiological streams such as electrocardiogram (ECG), heart rate, blood oxygen saturation (SpO(2)), and respiratory rate. Electronic health record initiatives around the world provide an environment for the electronic management of medical records, but they fail to support the high-frequency interpretation of streaming physiological data. We have taken a collaborative research approach to address this need to provide a flexible platform for the real-time online analysis of patients' data streams to detect medically significant conditions that precede the onset of medical complications. The platform supports automated or clinician-driven knowledge discovery to discover new relationships between physiological data stream events and latent medical conditions as well as to refine existing analytics. Patients benefit from the system because earlier detection of signs of the medical conditions may lead to earlier intervention that may potentially lead to improved patient outcomes and reduced length of stays. The clinician benefits from a decision support tool that provides insight into multiple streams of data that are too voluminous to assess with traditional methods. The remainder of this article summarizes the strengths of our research collaboration and the resulting environment known as Artemis, which is currently being piloted within the NICU of The Hospital for Sick Children (SickKids) in Toronto, Ontario, Canada. Although the discussion in this article focuses on a NICU, the technologies can be applied to any intensive care environment.

Autoregressive Hidden Markov Models for the Early Detection of Neonatal Sepsis

“…Note that this work still relies on laboratory results and thus is not being directed towards an earlier detection of the infection. The Artemis system [12], a stream computing project for neonatal intensive care, sets the detection of sepsis as one of its primary objectives. Their method introduces patient agents (PAs) able to perform multi-dimensional temporal abstraction on monitoring data [13].…”

Genomic technologies in neonatology

“…Иными словами, области трансляционной биоин-форматики отведена роль «мостика» между знаниями фундаментальных исследователей и врачей-клини-цистов, чтобы «принести инновации с лабораторно-го стола к постели пациента» («link innovations from bench to bedside») [51,52]. В частности, в Госпитале для больных детей (The Hospital for Sick Children) в Торонто (Канада) был осуществлен пилотный проект анализа жизненных показателей в режиме реального времени [53], а в другой работе было про-демонстрировано, что паттерны данных жизненных показателей могут помочь прогнозировать как кратко-срочную, так и долгосрочную заболеваемость у недо-ношенных детей [54].…”

NanoStreams: Codesigned microservers for edge analytics in real time

“…The concept of correlating such readings and even feeding these to a predictive mathematical model, is an active research topic in clinical medicine. Earlier research [21] showed the viability of physiological monitoring to detect sleep apnea in neo-natal ICU. NanoStreams extends the concept of utilising real time data to address the challenges of improving the standard of care by using a rapidly responsive automated surveillance system in an adult ICU.…”