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On the integration of an artifact system and a real-time healthcare analytics system

Authors:

Carolyn McGregor,

Rishikesan Kamaleswaran,

Jennifer Percival,

Nathan PercivalAuthors Info & Claims

IHI '10: Proceedings of the 1st ACM International Health Informatics Symposium

Pages 647 - 655

https://doi.org/10.1145/1882992.1883094

Published: 11 November 2010 Publication History

Abstract

As a result of advances in software technology, particularly stream computing, it is now possible to implement scalable systems capable of real-time analysis of multiple physiological data streams of multiple patients. There is a growing body of evidence showing that early onset indicators of some medical conditions can be observed as subtle changes in the physiological data streams of affected patients. These real-time healthcare analytics systems can detect the early onset indicators and thus may result in earlier detection of the medical condition which may lead to earlier intervention and improved patient outcomes. Blood draws and nasal suctioning can cause changes in the values of some physiological data stream elements. Such events, sometimes referred to as physiological stream artifacts can cause the real-time analytics systems to generate false alarms since the systems assume each data element is indicative the patient's underlying physiological condition. In order to minimize the generation of false alarms, artifact events must be captured and integrated in real time with the analytics result. We present the summary of an artifact study in a tertiary neonatal intensive care unit within a children's hospital where a real-time analytics system is being piloted as part of a clinical research study. We utilize the information gathered relating to the nature of these events and propose a framework to integrate the artifact events with the analytic results in real time

References

[1]

Griffin, P. and R. Moorman, "Toward the early diagnosis of neonatal sepsis and sepsis-like illness using novel heart rate analysis," Pediatrics, vol. 107, no. 1, pp. 97--104, 2001.

[2]

McInosh, N., J.-C. Becher, S. Cummingham, B. Stenson, A. Laing, A. J. Lyon, and P. Badger, "Clinical diagnosis of pneumothorax is late: Use of trend data and decision support might allow preclinical detection," Pediatric Research, vol. 48, no. 3, 408--415, 2000.

[3]

J. Fabres, W. A. Carlo, V. Phillips, G. Howard, and N. Ambalavanan, Both extremes of arterial carbon dioxide pressure and the magnitude of fluctuations in arterial carbon dioxide pressure are associated with severe intraventricular hemorrhage in preterm infants, Pediatrics, vol. 119, no. 2, pp. 299--305, 2007.

[4]

V. Tuzcu, S. Nas, U. Ulusar, A. Uger, and J. R. Kaiser, Altered Heart Rhythm Dynamics in Very Low Birth Weight Infants with Impending Intraventricular Haemorrhage, Pediatrics, vol. 123, no. 3, pp. 810--815, 2009.

[5]

Shankaran, S., J. C. Langer, N, Kazzi, A. R. Laptook, and M. Walsh, "Cumulative index of exposure to hypocarbia and hyperoxia as risk factors for preiventricular leukomalacia in low birth weight infants." Pediatrics, vol. 118, no. 4, pp. 1654--1659, 2006.

[6]

Claassen, J., S. A. Mayer, L. J. Hirsch, "Continuous EEG Monitoring in Patients with Subarachnoid Hemorrhage," Journal of Clinical Neurophysiology, vol. 22, no. 2, pp. 92--98.

[7]

Claassen, J., L. Hirsch, "ICU EEG monitoring for vasospasm and other focal cortical disorders," Handbook of Clinical Neurophysiology, vol. 8, pp. 864--880.

[8]

A. Bar-Or, J. Healey, L. Kontothanassis, J. M. Van Thong, BioStream: a system architecture for real-time processing of physiological signals, Conference Proceedings of IEEE Engineering in Medicine and Biology Society, no. 4, pp. 3101--3104, 2004.

[9]

H. Han, H. Ryoo, H. Patrick, An Infrastructure of Stream Data Mining, Fusion and Management for Monitored Patients, Proceedings 19th IEEE Symposium on Computer Based Medical Systems, pp. 461--468, 2006.

Digital Library

[10]

M. Blount, Ebling, M., Eklund, J. M., James, A., McGregor, C., Percival, N., Smith, K., Sow, D., Real-Time Analysis for Intensive Care: Development and Deployment of the Artemis Analytic System, IEEE Engineering in Medicine and Biology Magazine, pp. 110--118, March/April 2010.

[11]

H. Park, Jeong, D., Park, K., Automated Detection and Elimination of Periodic ECG Artifact in EEG Using the Energy Interval Histogram Method, IEEE Transactions of Biomedical Engineering, vol. 49, no. 12, pp. 1526--1533, December 2002.

[12]

Chambrin, M.-C. 2001 Review: alarms in the intensive care unit: how can the number of false alarms are reduced? Crit. Care 5, 184--188.

[13]

Lawless ST. Crying wolf: False alarms in a pediatric intensive care unit. Crit Car Med 1994; 22:981--985.

[14]

Takla, G., Petre, J. H., Doyle, D. J., Horibe, M. & Gopakumaran, B. 2006 The problem of artifacts in patient monitor data during surgery: a clinical and methodological review. Anesth. Analg. 103, 1196--1204.

[15]

C. Cao, I. Kohane, N. McIntosh, Artifact Detection in Cardiovascular Time Series Monitoring Data from Preterm Infants, AMIA Annual Symposium, 1999, 207 -211.

[16]

Q. Xue, Y. H. Hu, W. J. Tompkins, Neural network based adaptive matched filtering for QRS detection, IEEE Transaction Biomedical Engineering, no. 39, pp. 317--329, 1992.

[17]

J. Cheung, S. Hull, Detection of abnormal electrocardiograms using a neural network approach, Proceedings of the annual international conference of the IEEE Medicine and Biology Society, 1989.

[18]

C. Li, S. Wang, ECG detection method based on adaptive wavelet neural networks. Journal Biomedical Engineering, vol. 19, pp. 453--454, 2000.

[19]

H. Cao, P. Norris, A. Ozdas, J. Jenkins, J. A. Morris. A Simple Non-physiological Artifact Filter for Invasive Arterial Blood Pressure Monitoring: a Study of 1852 Trauma ICU Patients, 28th International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1417--1420, 2006.

[20]

M. P. Griffin, D. E. Lake, E. A. Bissonette, F. E. Harrell, T. M. O'Shea, J. R. Moorman, Heart Rate Characteristics: Novel Physiomarkers to Predict Neonatal Infection and Death, Pediatrics, vol. 116, no. 5, pp. 1070--1074, 2005.

Cited By

AM CJohnson Bressan N(2022)Frameworks and Platforms for Monitoring Animal Health and Wellness in Human Care and in the WildAn Introduction to Veterinary Medicine Engineering10.1007/978-3-031-22805-6_4(39-60)Online publication date: 16-Dec-2022
https://doi.org/10.1007/978-3-031-22805-6_4
McGregorAM CCibis T(2022)Frameworks and Platforms for Extreme Environments Adaptation and Resilience MonitoringEngineering and Medicine in Extreme Environments10.1007/978-3-030-96921-9_3(31-53)Online publication date: 14-Mar-2022
https://doi.org/10.1007/978-3-030-96921-9_3
Sutton JMahajan RAkbilgic OKamaleswaran R(2019)PhysOnline: An Open Source Machine Learning Pipeline for Real-Time Analysis of Streaming Physiological WaveformIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2018.283261023:1(59-65)Online publication date: Jan-2019
https://doi.org/10.1109/JBHI.2018.2832610
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Index Terms

On the integration of an artifact system and a real-time healthcare analytics system
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
2. Information systems
  1. Information systems applications
  2. World Wide Web
    1. Online advertising
    2. Web applications
      1. Electronic commerce

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Information

Published In

cover image ACM Other conferences

IHI '10: Proceedings of the 1st ACM International Health Informatics Symposium

November 2010

886 pages

ISBN:9781450300308

DOI:10.1145/1882992

Editor:
Tiffany Veinot
University of Michigan, USA
,
General Chairs:
Ümit V. Çatalyürek
The Ohio State University, USA
,
Gang Luo
IBM Research, USA
,
Program Chairs:
Henrique Andrade
Goldman Sachs, USA
,
Neil R. Smalheiser
University of Illinois, Chicago, USA

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGHIT: ACM Special Interest Group on Health Informatics

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 November 2010

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IHI '10: ACM International Health Informatics Symposium

November 11 - 12, 2010

Virginia, Arlington, USA

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Cited By

AM CJohnson Bressan N(2022)Frameworks and Platforms for Monitoring Animal Health and Wellness in Human Care and in the WildAn Introduction to Veterinary Medicine Engineering10.1007/978-3-031-22805-6_4(39-60)Online publication date: 16-Dec-2022
https://doi.org/10.1007/978-3-031-22805-6_4
McGregorAM CCibis T(2022)Frameworks and Platforms for Extreme Environments Adaptation and Resilience MonitoringEngineering and Medicine in Extreme Environments10.1007/978-3-030-96921-9_3(31-53)Online publication date: 14-Mar-2022
https://doi.org/10.1007/978-3-030-96921-9_3
Sutton JMahajan RAkbilgic OKamaleswaran R(2019)PhysOnline: An Open Source Machine Learning Pipeline for Real-Time Analysis of Streaming Physiological WaveformIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2018.283261023:1(59-65)Online publication date: Jan-2019
https://doi.org/10.1109/JBHI.2018.2832610
Nizami SBasharat AShoukat AHameed URaza SBekele AGiffen RGreen J(2018)CEA: Clinical Event Annotator mHealth Application for Real-time Patient Monitoring2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)10.1109/EMBC.2018.8512898(2921-2924)Online publication date: Jul-2018
https://doi.org/10.1109/EMBC.2018.8512898
Ansari SBelle AGhanbari HSalamango MNajarian K(2016)Suppression of false arrhythmia alarms in the ICU: a machine learning approachPhysiological Measurement10.1088/0967-3334/37/8/118637:8(1186-1203)Online publication date: 25-Jul-2016
https://doi.org/10.1088/0967-3334/37/8/1186
Percival JMcgregor CPercival NJames A(2015)Enabling the integration of clinical event and physiological data for real-time and retrospective analysisInformation Systems and e-Business Management10.1007/s10257-014-0232-913:4(693-711)Online publication date: 1-Nov-2015
https://dl.acm.org/doi/10.1007/s10257-014-0232-9
Huang WMcGregor CJames A(2014)A comprehensive framework design for continuous quality improvement within the neonatal intensive care unit: Integration of the SPOE, CRISP-DM and PaJMa modelsIEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)10.1109/BHI.2014.6864360(289-292)Online publication date: Jun-2014
https://doi.org/10.1109/BHI.2014.6864360
Catley CSmith KMcGregor CJames AEklund J(2013)A Framework for Multidimensional Real-Time Data AnalysisMethods, Models, and Computation for Medical Informatics10.4018/978-1-4666-2653-9.ch002(16-35)Online publication date: 2013
https://doi.org/10.4018/978-1-4666-2653-9.ch002
Nizami SGreen JMcGregor C(2013)Implementation of Artifact Detection in Critical Care: A Methodological ReviewIEEE Reviews in Biomedical Engineering10.1109/RBME.2013.22437246(127-142)Online publication date: 2013
https://doi.org/10.1109/RBME.2013.2243724
Catley CSmith KMcGregor CJames AEklund J(2011)A Framework for Multidimensional Real-Time Data AnalysisInternational Journal of Computational Models and Algorithms in Medicine10.4018/jcmam.20110101022:1(16-37)Online publication date: 1-Jan-2011
https://doi.org/10.4018/jcmam.2011010102
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