Expediting assessments of database performance for streams of respiratory parameters

Gillan, Charles J.; Novaković, Aleksandar; Marshall, A.H.; Shyamsundar, Murali; Nikolopoulos, Dimitrios S.

doi:10.1016/j.compbiomed.2018.05.028

Cited by 8 publications

(5 citation statements)

References 22 publications

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“…It was deployed at the Royal Victoria Hospital, Belfast, with the goal to continuously monitor patient tidal volume (TVs) against a set of thresholds and provide the appropriate real time alerts when the system predicts that the configured ventilatory settings may lead to VILI. As it was operating in the Regional ICU which can admit up to a maximum of 20 patients, previous work investigated its scalability potential to adopt the system on a hospital wide level, and got very promising results [12].…”

Section: Introductionmentioning

confidence: 99%

Analysing the Performance of a Real-Time Healthcare 4.0 System using Shared Frailty Time to Event Models

Marshall

Novaković

2019

2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)

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This paper introduces the real-time Healthcare 4.0 system, the VILIAlert system and a new approach that we propose for the robust assessment of it's performance. The VILIAlert system alerts clinicians when a patient's tidal volume value rises above the clinically accepted level of 8 ml/kg as beyond this point (> 8 ml/kg), a patient is considered high risk of permanent damage to their lungs. In order to ensure success with the VILIAlert system, the ideal scenario is to ensure that as soon as patients in the Intensive Care Unit experience tidal volume values beyond the 8 ml/kg level, a clinical intervention can be carried out so to minimise the risk of patients ever having permanent damage. The approach has been implemented in the Intensive Care Unit at the Royal Victoria Hospital Belfast, Northern Ireland demonstrating the potential for such an approach to be used across all hospitals in the region.

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Section: Introductionmentioning

confidence: 99%

Analysing the Performance of a Real-Time Healthcare 4.0 System using Shared Frailty Time to Event Models

Marshall

Novaković

2019

2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)

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“…In a previous paper we introduced the VILIAlert system [ 38 ], a quality improvement project, presenting an analysis of the performance of the database systems which underpin the collection of streams of patient respiratory data. The data was collected in the Regional Intensive Care Unit (RICU), Royal Victoria Hospital, Belfast over a three year period.…”

Section: Methodsmentioning

confidence: 99%

Comparing regression and neural network techniques for personalized predictive analytics to promote lung protective ventilation in Intensive Care Units

Hagan

Gillan

Spence

et al. 2020

Computers in Biology and Medicine

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Mechanical ventilation is a lifesaving tool and provides organ support for patients with respiratory failure. However, injurious ventilation due to inappropriate delivery of high tidal volume can initiate or potentiate lung injury. This could lead to acute respiratory distress syndrome, longer duration of mechanical ventilation, ventilator associated conditions and finally increased mortality. In this study, we explore the viability and compare machine learning methods to generate personalized predictive alerts indicating violation of the safe tidal volume per ideal body weight (IBW) threshold that is accepted as the upper limit for lung protective ventilation (LPV), prior to application to patients. We process streams of patient respiratory data recorded per minute from ventilators in an intensive care unit and apply several state-of-the-art time series prediction methods to forecast the behavior of the tidal volume metric per patient, 1 hour ahead. Our results show that boosted regression delivers better predictive accuracy than other methods that we investigated and requires relatively short execution times. Long short-term memory neural networks can deliver similar levels of accuracy but only after much longer periods of data acquisition, further extended by several hours computing time to train the algorithm. Utilizing Artificial Intelligence, we have developed a personalized clinical decision support tool that can predict tidal volume behavior within accuracy and compare alerts recorded from a real world system to highlight that our models would have predicted violations 1 hour ahead and can therefore conclude that the algorithms can provide clinical decision support.

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“…Os valores para ambas as métricas foram coletados de acordo com os dados disponibilizado pela ferramenta de teste de carga utilizada, o JMeter 1 . Escolhemos essa ferramenta, pois a mesma foi utilizada em alguns trabalhos relacionados a este, como [Gillan et al 2018] e [Gaur et al 2017].…”

Section: Tabela 3 Fatores E Níveis Do Doe Para a Métrica Tempo De Exunclassified

Uma Avaliação de Desempenho de Contêineres Docker Executando Diferentes SGBDs Relacionais

Santos¹,

Endo²,

Silva³

2019

Anais Do Workshop Em Desempenho De Sistemas Computacionais E De Comunicação (WPerformance)

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O conceito de virtualização computacional, apesar de antigo, se tornou lugar-comum na viabilização de aplicações que compartilham recursos computacionais na infraestrutura da nuvem. As máquinas virtuais (do inglês virtual machines, VMs) trouxeram benefı́cios como a possibilidade de realizar live migration para prover tolerância a falha e balanceamento de carga. Porém, apesar das vantagens, as VMs adicionam camadas extras de abstração, resultando em perda de eficiência. Como alternativa as VMs, atualmente os contêineres se apresentam como uma solução mais leve e com bom desempenho. Este trabalho realiza uma avaliação de desempenho baseada em análise de sensibilidade com o objetivo de identificar os fatores que mais impactam na eficiência de um Sistema de Gerenciamento de Banco de Dados (SGBD) utilizando Docker como contêiner.

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Expediting assessments of database performance for streams of respiratory parameters

Cited by 8 publications

References 22 publications

Analysing the Performance of a Real-Time Healthcare 4.0 System using Shared Frailty Time to Event Models

Analysing the Performance of a Real-Time Healthcare 4.0 System using Shared Frailty Time to Event Models

Comparing regression and neural network techniques for personalized predictive analytics to promote lung protective ventilation in Intensive Care Units

Uma Avaliação de Desempenho de Contêineres Docker Executando Diferentes SGBDs Relacionais

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