Clinical monitoring using regression-based trend templates

Haimowitz, Ira J.; Le, Panin; Kohane, Isaac S.

doi:10.1016/0933-3657(95)00023-6

Cited by 57 publications

(23 citation statements)

References 16 publications

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“…Specifically, our approach is to use temporal trend templates (Coiera, 1990;Haimowitz, Le, & Kohane, 1995;Salatian & Hunter, 1996). The goal is to develop a system that provides: (a) algorithms for automatically identifying and interpreting relevant monitored patterns and artefact; (b)…”

Section: Implications For Human Computer Interactionmentioning

confidence: 99%

Expertise and the interpretation of computerized physiological data: implications for the design of computerized monitoring in neonatal intensive care

Alberdi

Becher

Gilhooly

et al. 2001

International Journal of Human-Computer Studies

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This is the unspecified version of the paper.This version of the publication may differ from the final published version. and senior physicians in their interpretation of monitored physiological data. It was found that the senior doctors made better use than the junior doctors of the different knowledge sources available. The senior doctors were able to identify more relevant physiological patterns and generated more and better inferences than did their junior colleagues. Expertise differences are discussed in the context of previous psychological research in medical expertise. Finally, the paper discusses the potential utility of these outcomes to inform the design of computerised decision support in neonatal intensive care. Permanent repository link

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Section: Implications For Human Computer Interactionmentioning

confidence: 99%

Expertise and the interpretation of computerized physiological data: implications for the design of computerized monitoring in neonatal intensive care

Alberdi

Becher

Gilhooly

et al. 2001

International Journal of Human-Computer Studies

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“…In this type of work, the goal is to describe what is happening, and sometimes to suggest what to do next; an interpretation is put on the data. Different schemes for heuristic description of patterns have been used, see for example [30]- [32].…”

Section: A Relation To Previous Work On Icu Monitoringmentioning

confidence: 99%

Factorial Switching Linear Dynamical Systems Applied to Physiological Condition Monitoring

Quinn

Williams

McIntosh

2009

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-Condition monitoring often involves the analysis of systems with hidden factors that switch between different modes of operation in some way. Given a sequence of observations, the task is to infer the filtering distribution of the switch setting at each time step. In this paper we present factorial switching linear dynamical systems as a general framework for handling such problems. We show how domain knowledge and learning can be successfully combined in this framework, and introduce a new factor (the "X-factor") for dealing with unmodelled variation.We demonstrate the flexibility of this type of model by applying it to the problem of monitoring the condition of a premature baby receiving intensive care. The state of health of a baby cannot be observed directly, but different underlying factors are associated with particular patterns of physiological measurements and artifacts. We have explicit knowledge of common factors and use the X-factor to model novel patterns which are clinically significant but have unknown cause. Experimental results are given which show the developed methods to be effective on typical intensive care unit monitoring data.

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“…TAs can then be interpreted by matching against pre-defined templates or guidelines to aid in the care of patients in real time [8,9,24]. In this way, TA [25] transforms patient data from low-level quantitative data in the form of physiological time series to high-level qualitative descriptions that are closer to the language of clinicians.…”

Section: Real Time Temporal Abstraction-based Event Processingmentioning

confidence: 99%

Next generation remote critical care through service-oriented architectures: challenges and opportunities

McGregor

Eklund

2010

SOCA

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Health care providers and governments are under pressure to maintain and improve the quality of care to an increasing volume of critical care patients at either end of the life cycle, namely premature and ill term babies together with the elderly. The provision of a service of critical care utilizing real time service-oriented architectures has the potential to enable clinicians to be supported in the care of a greater number patients that are, perhaps more importantly, located elsewhere to their intensive care units. This paper presents a review of recent research in the application of computing and IT to support the service of critical care and determines the trends and challenges for the application of real time serviceoriented architectures within the domain. It then presents some case study-based research on the design of a serviceoriented architecture-based approach to support two aspects of critical care namely elderly care and neonatal intensive care to provide further context to trends and opportunities.

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Clinical monitoring using regression-based trend templates

Cited by 57 publications

References 16 publications

Expertise and the interpretation of computerized physiological data: implications for the design of computerized monitoring in neonatal intensive care

Expertise and the interpretation of computerized physiological data: implications for the design of computerized monitoring in neonatal intensive care

Factorial Switching Linear Dynamical Systems Applied to Physiological Condition Monitoring

Next generation remote critical care through service-oriented architectures: challenges and opportunities

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