An Infrastructure of Stream Data Mining, Fusion and Management for Monitored Patients

Han, Hugang; Ryoo, Han C.; Patrick, Herbert

doi:10.1109/cbms.2006.39

Cited by 22 publications

(4 citation statements)

References 3 publications

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“…The goal of the group was to develop the prototype and collaborate with a medical institution on a pilot study. A Drexel University research team set out to design a system that performed online continuous processing of an ICU patient’s data stream and data capture to perform offline analysis to develop new clinical hypotheses [ 40 ].…”

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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“…The main thesis of most studies is that the complexities characterizing critical illness (often with comorbidities) cannot be adequately addressed using traditional approaches (e.g., single drug interventions), and perhaps the use of big data-driven data fusion approaches is more suitable (Johnson et al 2016;Sanchez-Pinto et al 2018). Examples of big data applications in critical care include a scalable infrastructure for developing a patient care management system which combines static and continuous data monitored from critically ill patients in the ICU for data mining and alerting medical staff of critical events in real time (Han et al 2006), a system to predict increased intracranial pressure in the ICU (Güiza et al 2013), as well as a system developed for a neonatal ICU which utilized streaming data from EEG monitors, infusion pumps, cerebral oxygenation monitors, etc. to provide medical decision support (Bressan et al 2012).…”

Section: Applications In Continuous Patient Monitoringmentioning

confidence: 99%

A review of big data applications of physiological signal data

Orphanidou

2019

Biophys Rev

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The proliferation of smart physiological signal monitoring sensors, combined with the advancement of telemetry and intelligent communication systems, has led to an explosion in healthcare data in the past few years. Additionally, access to cheaper and more effective power and storage mechanisms has significantly increased the availability of healthcare data for the development of big data applications. Big data applications in healthcare are concerned with the analysis of datasets which are too big, too fast, and too complex for healthcare providers to process and interpret with existing tools. The driver for the development of such systems is the continuing effort in making healthcare services more efficient and sustainable. In this paper, we provide a review of current big data applications which utilize physiological waveforms or derived measurements in order to provide medical decision support, often in real time, in the clinical and home environment. We focus mainly on systems developed for continuous patient monitoring in critical care and discuss the challenges that need to be overcome such that these systems can be incorporated into clinical practice. Once these challenges are overcome, big data systems have the potential to transform healthcare management in the hospital of the future.

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“…Hence, the potential for developing CDSS in an ICU environment has been recognized by many researchers. A scalable infrastructure for developing a patient care management system has been proposed which combines static data and stream data monitored from critically ill patients in the ICU for data mining and alerting medical staff of critical events in real time [ 113 ]. Similarly, Bressan et al developed an architecture specialized for a neonatal ICU which utilized streaming data from infusion pumps, EEG monitors, cerebral oxygenation monitors, and so forth to provide clinical decision support [ 114 ].…”

Section: Medical Signal Analyticsmentioning

confidence: 99%

Big Data Analytics in Healthcare

Belle

Raghuram

Soroushmehr

et al. 2015

BioMed Research International

403

192

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The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently applied towards aiding the process of care delivery and disease exploration. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm. In this paper, we discuss some of these major challenges with a focus on three upcoming and promising areas of medical research: image, signal, and genomics based analytics. Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed. Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined.

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An Infrastructure of Stream Data Mining, Fusion and Management for Monitored Patients

Cited by 22 publications

References 3 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

A review of big data applications of physiological signal data

Big Data Analytics in Healthcare

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