Toward a Big Data Healthcare Analytics System: A Mathematical Modeling Perspective

Khazaei, Hamzeh; McGregor, Carolyn; Eklund, Mikael; El-Khatib, Khalil; Thommandram, Anirudh

doi:10.1109/services.2014.45

Cited by 14 publications

(8 citation statements)

References 14 publications

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“…The Artemis-IC provides HAaaS for concurrent multipatient, multistream, and multidiagnosis through temporal analysis to support real-time clinical decision support and clinical research [ 22 , 23 ]. We deployed a pilot project by implementing Artemis-IC at Toronto’s SickKids hospital and proposed an analytical model [ 24 ] to enable performance evaluation and capacity planning in advance of final deployment. In addition, there is another pilot of the Artemis-IC at Women and Infants Hospital of Rhode Island (WIHRI), which is collecting physiological data for analytical and simulation modeling purposes.…”

Section: Methodsmentioning

confidence: 99%

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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: Methodsmentioning

confidence: 99%

“…As the population size of newborns is relatively high while the probability that a given newborn baby to be preterm is relatively small, the arrival process can be modeled as a Poisson process. The details of the performance modeling can be found in [ 24 ].…”

Section: Methodsmentioning

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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“…Hayes et al [21] proposed an analytical model for a proposed infrastructure which is supporting an in-house deployment of Artemis. Khazaei et al [9] modeled a version of Artemis project deployed at SickKids Hospital in Toronto; however that modeling is specific to SickKids’s NICU which has different types of patients, monitors, and analytics compared to McMaster Children’s Hospital. In addition, that work did not incorporate load testing that included smart infusion pump data.…”

Section: Related Workmentioning

confidence: 99%

“…However, to be able to correctly size implementations on a per hospital basis, based on the number of beds and patient characteristics, the creation of an analytical model to enable capacity planning for the usage of such a platform is required. Analytical models within the context of cloud based big data solutions is currently an under researched area, especially within the context of its use in healthcare [9] .…”

Section: Introductionmentioning

confidence: 99%

Health Informatics for Neonatal Intensive Care Units: An Analytical Modeling Perspective

Khazaei

Mench-Bressan

McGregor

et al. 2015

IEEE J. Transl. Eng. Health Med.

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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.

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“…Thereafter, the Artemis network grew rapidly connecting several NICUs at hospitals located worldwide [32,33]. Recent network expansions include cloud-based implementation [34,35], and remote monitoring applications [36]. Artemis has the ability to capture, store and analyze multivariate physiologic data streams for simultaneous diagnosis and prediction of multiple clinical events [31].…”

Section: Research Motivationmentioning

confidence: 99%

Integration of Artifact Detection in Clinical Decision Support Systems

Nizami¹

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Artifact detection (AD) is a critical component of Clinical Decision Support Systems (CDSS) to assess the quality of data and issue clinical alarms with high specificity and low false alarm rates. An in-depth review of the literature reveals that, although many advanced AD algorithms have been developed, very few have been evaluated in a realtime environment or reached actual clinical implementation. Furthermore, no single AD algorithm is necessarily best-suited to serve the varying needs of a CDSS. This identifies the need to develop and evaluate a framework that supports the interoperability of a range of configurable AD algorithms that could be integrated with CDSS. This thesis develops an AD framework to address six different shortcomings identified by the literature review. This framework supports the following six features: (f.1) Flexibility to serve the needs of patient populations from different types of Critical Care Units (CCU) through generalization and customizability; (f.2) Reusability across multiple types of physiologic data harvested by different Original Equipment Manufacturer (OEM) monitors; (f.3) Standardized definitions of Signal Quality Indicators (SQI) that promote interoperability and comparison between independently developed components; (f.4) Reusability and scalability by mixing and matching several AD, Parameter Derivation (PD), and Clinical Event Detection (CED) components in various combinations; (f.5) Customizability to evaluate and compare the performance of multiple combinations of independently developed components on offline and potentially real-time patient data when integrated with clinical workflows; and (f.6) Standardized component interfaces that can potentially support real-time clinical implementation of AD. The developed framework provides for a unique test bed with the ability to create new AD composition pipelines by mixing and matching independently developed I would like to express profound gratitude towards my thesis supervisors, Professor James R. Green and Professor Carolyn McGregor. They have both provided comprehensive guidance and tremendous support during my doctoral journey at Carleton.

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Toward a Big Data Healthcare Analytics System: A Mathematical Modeling Perspective

Cited by 14 publications

References 14 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

Health Informatics for Neonatal Intensive Care Units: An Analytical Modeling Perspective

Integration of Artifact Detection in Clinical Decision Support Systems

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