Towards Health Data Stream Analytics

Zhang, Qing; Pang, Chaoyi; McBride, Simon; Hansen, David; Cheung, Christina; Steyn, Michael

doi:10.1109/iccme.2010.5558827

Cited by 18 publications

(7 citation statements)

References 23 publications

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“…While there has been a growing body of biomedical engineering and clinical research over the past 20-30 years proposing newer approaches for advanced physiological stream monitoring, they still predominantly have a physiological stream, clinical condition, or patient-centric approach [ 37 ]. Zhang et al [ 38 ] have discussed the implementation of a Health Data Stream Analytics System called the “Anesthetics Data Analyzer,” which has been developed to provide anesthetists with the ability to monitor and query trends in physiological signals data, a kind of stream data from the health care domain.…”

Section: Discussionmentioning

confidence: 99%

Real-Time and Retrospective Health-Analytics-as-a-Service: A Novel Framework

Khazaei

McGregor²,

Eklund³

et al. 2015

JMIR Med Inform

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BackgroundAnalytics-as-a-service (AaaS) is one of the latest provisions emerging from the cloud services family. Utilizing this paradigm of computing in health informatics will beneﬁt patients, care providers, and governments signiﬁcantly. This work is a novel approach to realize health analytics as services in critical care units in particular.ObjectiveTo design, implement, evaluate, and deploy an extendable big-data compatible framework for health-analytics-as-a-service that offers both real-time and retrospective analysis.MethodsWe present a novel framework that can realize health data analytics-as-a-service. The framework is flexible and conﬁgurable for different scenarios by utilizing the latest technologies and best practices for data acquisition, transformation, storage, analytics, knowledge extraction, and visualization. We have instantiated the proposed method, through the Artemis project, that is, a customization of the framework for live monitoring and retrospective research on premature babies and ill term infants in neonatal intensive care units (NICUs).ResultsWe demonstrated the proposed framework in this paper for monitoring NICUs and refer to it as the Artemis-In-Cloud (Artemis-IC) project. A pilot of Artemis has been deployed in the SickKids hospital NICU. By infusing the output of this pilot set up to an analytical model, we predict important performance measures for the ﬁnal deployment of Artemis-IC. This process can be carried out for other hospitals following the same steps with minimal effort. SickKids’ NICU has 36 beds and can classify the patients generally into 5 different types including surgical and premature babies. The arrival rate is estimated as 4.5 patients per day, and the average length of stay was calculated as 16 days. Mean number of medical monitoring algorithms per patient is 9, which renders 311 live algorithms for the whole NICU running on the framework. The memory and computation power required for Artemis-IC to handle the SickKids NICU will be 32 GB and 16 CPU cores, respectively. The required amount of storage was estimated as 8.6 TB per year. There will always be 34.9 patients in SickKids NICU on average. Currently, 46% of patients cannot get admitted to SickKids NICU due to lack of resources. By increasing the capacity to 90 beds, all patients can be accommodated. For such a provisioning, Artemis-IC will need 16 TB of storage per year, 55 GB of memory, and 28 CPU cores.ConclusionsOur contributions in this work relate to a cloud architecture for the analysis of physiological data for clinical decisions support for tertiary care use. We demonstrate how to size the equipment needed in the cloud for that architecture based on a very realistic assessment of the patient characteristics and the associated clinical decision support algorithms that would be required to run for those patients. We show the principle of how this could be performed and furthermore that it can be replicated for any critical care setting within a tertiary institution.

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Section: Discussionmentioning

confidence: 99%

Real-Time and Retrospective Health-Analytics-as-a-Service: A Novel Framework

Khazaei

McGregor²,

Eklund³

et al. 2015

JMIR Med Inform

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“…to check the driver profile and also to monitor the health of driver during driving [17]. There are number of driving problems like 417 teen age drivers always run the vehicle at very high speed, that's why, highest crash rate.…”

Section: Area Of I Mplementation To Be C Onsideredmentioning

confidence: 98%

Theoretical framework for physiological profiling using sensors and Big Data Analytics

Gupta

Jyoti²

2015

2015 Communication, Control and Intelligent Systems (CCIS)

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In today's world, everybody has limited time and generally do rash driving to reach at destination. So intentionally or unintentionally many crashes occurred and generally get mentally upset which leads to more crime or health disturbance.Due to work pressure and unavoidable environment, some changes in hormones have been occurred. This paper is based on analysis of physiological profile of drivers with the help of smart phone based application. Sensors will collect the real time in vehicle data which would be analyzed with the help of Big Data Analytics. Driver's behavior is affected by number of factors.This paper will discuss about that number of factors which affects the nature of a person. We examine an algorithm to check whether the behavior of driver is normal or abnormal. This will help the administration to analyze the chances of crashes or crimes that may occurred due to abnormal behavior and to some extent, things can be controlled.

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“…(Mathioudakis & Koudas, 2010). Because of the nature of this stream data, which continuously and rapidly grow over time, the difficulties to develop streaming data analytics are multifaceted (Zhang et al, 2010).…”

Section: Related Workmentioning

confidence: 99%

“…The main reasons for this difficulty are mainly due to stream data being too "Big" to be processed on memory and stored in a single database. Conventional technologies to deploy streaming analytics are Relational DataBase Management Systems such as Oracle, MySQL, and DB2 which have been used to store stream data and allow batch processing queries (Ranjan, 2014;Zhang et al, 2010). Moreover, they were originally designed for structured data on a single computing unit.…”

Section: Introductionmentioning

confidence: 99%

Towards Real-time Streaming Analytics based on Cloud Computing

Cha¹

2015

STBD

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Nowadays, streaming data overflows from a diversity of sources and technologies, making traditional data analytics technologies unsuitable to handle the latency of data processing relative to the growing demand for high processing speed and algorithmically scalability. Real-time streaming data analytics is needed to allow applications to analyze streaming data effectively and efficiently. The open source software Apache Storm, which is a distributed computation system for processing streaming data in real-time, has been widely used for building applications to analyze streaming data in real-time because it is fast, scalable, fault tolerant and reliable. This paper proposes a cloud-based architecture based on Apache Storm for supporting an entire streaming data analytics workflow, which consists of data ingestion, data processing, data visualization and data storing. The application on the proposed system is available at http://206.167.181.16:8000/map4.html.

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Towards Health Data Stream Analytics

Cited by 18 publications

References 23 publications

Real-Time and Retrospective Health-Analytics-as-a-Service: A Novel Framework

Real-Time and Retrospective Health-Analytics-as-a-Service: A Novel Framework

Theoretical framework for physiological profiling using sensors and Big Data Analytics

Towards Real-time Streaming Analytics based on Cloud Computing

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