An Artificial Immune System Approach for a Multi-compartment Queuing Model for Improving Medical Resources and Inpatient Bed Occupancy in Pandemics

Belciug, Smaranda; Bejinariu, Silviu-Ioan; Costin, Hariton

doi:10.4316/aece.2020.03003

“…Eleven of the 14 studies investigated predictive models and were assessed according to PROBAST and TRIPOD: eight studies developed prognostic models [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ] and three studies developed diagnostic models [ 38 , 39 , 40 ]. Of the remaining three studies, two evaluated the prognostic potential of existing AI-based lung segmentation software (without integration into a multivariate predictive model) [ 41 , 42 ] and one investigated an AI-based system for resource optimisation in the ICU [ 43 ]. Eleven studies used patient data collected from the ICU and four studies used data from the ED.…”

Section: Resultsmentioning

confidence: 99%

“…Apart from diagnostic and prognostic applications, Belciug, et al [ 43 ] utilised an artificial immune system algorithm, a type of evolutionary AI algorithm, to optimise a queueing model for simulating hospital bed allocation in the ICU. The final model, intended as a tool for hospital managers, proposes an optimal admission rate and number of beds while balancing the costs associated with increasing capacity and refusing patients.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review

Chee

¹

,

Ong

²

,

Siddiqui

³

et al. 2021

IJERPH

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Background: Little is known about the role of artificial intelligence (AI) as a decisive technology in the clinical management of COVID-19 patients. We aimed to systematically review and critically appraise the current evidence on AI applications for COVID-19 in intensive care and emergency settings. Methods: We systematically searched PubMed, Embase, Scopus, CINAHL, IEEE Xplore, and ACM Digital Library databases from inception to 1 October 2020, without language restrictions. We included peer-reviewed original studies that applied AI for COVID-19 patients, healthcare workers, or health systems in intensive care, emergency, or prehospital settings. We assessed predictive modelling studies and critically appraised the methodology and key findings of all other studies. Results: Of fourteen eligible studies, eleven developed prognostic or diagnostic AI predictive models, all of which were assessed to be at high risk of bias. Common pitfalls included inadequate sample sizes, poor handling of missing data, failure to account for censored participants, and weak validation of models. Conclusions: Current AI applications for COVID-19 are not ready for deployment in acute care settings, given their limited scope and poor quality. Our findings underscore the need for improvements to facilitate safe and effective clinical adoption of AI applications, for and beyond the COVID-19 pandemic.

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“…However, current applications within the reviewed articles mainly comprised prognostic models for critical illness or diagnostic models to predict COVID-19 status, none of which are ready for clinical use. Only one preliminary study by Belciug et al 40 , which lacked validation, investigated allocative simulation and resource optimisation in the ICU, while no study investigated automatic monitoring or prognostication of COVID-19 patients. Belciug et al’s study on ICU resource optimisation employed queueing theory, a mathematical field of study, and Artificial Immune Systems, an evolutionary AI algorithm that is uncommonly utilised in medical research.…”

Section: Discussionmentioning

confidence: 99%

“…11 of the 14 studies investigated predictive models and were assessed according to PROBAST and TRIPOD: eight studies developed prognostic models [27][28][29][30][31][32][33][34] and three studies developed diagnostic models [35][36][37] . Of the remaining three studies, two evaluated the prognostic potential of existing AI-based lung segmentation software (without integration into a multivariate predictive model) 38,39 and one investigated an AI-based system for resource optimisation in the ICU 40 . Eleven studies used patient data collected from the ICU and four studies used data from the ED.…”

Section: Study Characteristicsmentioning

confidence: 99%

“…Apart from diagnostic and prognostic applications, Belciug et al 40 utilised an Artificial Immune System algorithm, a type of evolutionary AI algorithm, to optimise a queueing model for simulating hospital bed allocation in the ICU. The final model, intended as a tool for hospital managers, proposes an optimal admission rate and number of beds while balancing the costs associated with increasing capacity and refusing patients.…”

Section: Other Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review

Chee

¹

,

Ong

²

,

Siddiqui

³

et al. 2021

Preprint

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Background: Little is known about the role of artificial intelligence (AI) as a decisive technology in the clinical management of COVID-19 patients. We aimed to systematically review and critically appraise the current evidence on AI applications for COVID-19 in intensive care and emergency settings, focusing on methods, reporting standards, and clinical utility. Methods: We systematically searched PubMed, Embase, Scopus, CINAHL, IEEE Xplore, and ACM Digital Library databases from inception to 1 October 2020, without language restrictions. We included peer-reviewed original studies that applied AI for COVID-19 patients, healthcare workers, or health systems in intensive care, emergency or prehospital settings. We assessed predictive modelling studies using PROBAST (prediction model risk of bias assessment tool) and a modified TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) statement for AI. We critically appraised the methodology and key findings of all other studies. Results: Of fourteen eligible studies, eleven developed prognostic or diagnostic AI predictive models, all of which were assessed to be at high risk of bias. Common pitfalls included inadequate sample sizes, poor handling of missing data, failure to account for censored participants, and weak validation of models. Studies had low adherence to reporting guidelines, with particularly poor reporting on model calibration and blinding of outcome and predictor assessment. Of the remaining three studies, two evaluated the prognostic utility of deep learning-based lung segmentation software and one studied an AI-based system for resource optimisation in the ICU. These studies had similar issues in methodology, validation, and reporting. Conclusions: Current AI applications for COVID-19 are not ready for deployment in acute care settings, given their limited scope and poor quality. Our findings underscore the need for improvements to facilitate safe and effective clinical adoption of AI applications, for and beyond the COVID-19 pandemic.

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Bed-Occupancy Management and Hospital Planning: A Handbook

Belciug

¹

2022

Translational Bioinformatics

0

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An Artificial Immune System Approach for a Multi-compartment Queuing Model for Improving Medical Resources and Inpatient Bed Occupancy in Pandemics

Cited by 11 publications

References 25 publications

Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review

Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review

Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review

Bed-Occupancy Management and Hospital Planning: A Handbook

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