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

Chee, Marcel Lucas; Ong, Marcus Eng Hock; Siddiqui, Fahad Javaid; Zhang, Zhongheng; Lim, S.C.; Ho, Andrew Fu Wah; Liu, Nan

doi:10.3390/ijerph18094749

Cited by 25 publications

(16 citation statements)

References 65 publications

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“…The application of AI in HEP is mainly focused on infectious or parasitic diseases (N = 13) [117] , [118] , [119] , [120] , [121] , [122] , [123] , [124] , [125] , [126] , [127] , [128] and secondary on factors influencing health (N = 2) [129] , [130] and mental health (N = 1) [131] , as depicted in Fig. 3 .…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews

Martínez-Millana

Sáez

Tornero

et al. 2022

International Journal of Medical Informatics

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Section: Resultsmentioning

confidence: 99%

Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews

Martínez-Millana

Sáez

Tornero

et al. 2022

International Journal of Medical Informatics

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“…The COVID-19 research is quickly moving forward. Each day hundreds of new papers are published [ 95 , 96 , 97 ]. As AI starts to play an increasingly important role in clinical practice [ 98 , 99 ], it is crucial to evaluate its performance correctly.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence for COVID-19 Detection in Medical Imaging—Diagnostic Measures and Wasting—A Systematic Umbrella Review

Jemioło

Storman

Orzechowski

2022

JCM

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The COVID-19 pandemic has sparked a barrage of primary research and reviews. We investigated the publishing process, time and resource wasting, and assessed the methodological quality of the reviews on artificial intelligence techniques to diagnose COVID-19 in medical images. We searched nine databases from inception until 1 September 2020. Two independent reviewers did all steps of identification, extraction, and methodological credibility assessment of records. Out of 725 records, 22 reviews analysing 165 primary studies met the inclusion criteria. This review covers 174,277 participants in total, including 19,170 diagnosed with COVID-19. The methodological credibility of all eligible studies was rated as critically low: 95% of papers had significant flaws in reporting quality. On average, 7.24 (range: 0–45) new papers were included in each subsequent review, and 14% of studies did not include any new paper into consideration. Almost three-quarters of the studies included less than 10% of available studies. More than half of the reviews did not comment on the previously published reviews at all. Much wasting time and resources could be avoided if referring to previous reviews and following methodological guidelines. Such information chaos is alarming. It is high time to draw conclusions from what we experienced and prepare for future pandemics.

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“…The ED visits dated in 2019 are assigned to one testing cohort, while those dated in 2020 are assigned to a second testing cohort covering the period of the COVID-19 pandemic [ 40 , 41 ]. Using this sequential testing design, we will be able to test whether the population shift and the COVID-19 pandemic would impact model performance [ 42 ]. Further details are presented below.…”

Section: Methodsmentioning

confidence: 99%

Leveraging Large-Scale Electronic Health Records and Interpretable Machine Learning for Clinical Decision Making at the Emergency Department: Protocol for System Development and Validation

Liu¹,

Xie²,

Siddiqui³

et al. 2022

JMIR Res Protoc

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Background There is a growing demand globally for emergency department (ED) services. An increase in ED visits has resulted in overcrowding and longer waiting times. The triage process plays a crucial role in assessing and stratifying patients’ risks and ensuring that the critically ill promptly receive appropriate priority and emergency treatment. A substantial amount of research has been conducted on the use of machine learning tools to construct triage and risk prediction models; however, the black box nature of these models has limited their clinical application and interpretation. Objective In this study, we plan to develop an innovative, dynamic, and interpretable System for Emergency Risk Triage (SERT) for risk stratification in the ED by leveraging large-scale electronic health records (EHRs) and machine learning. Methods To achieve this objective, we will conduct a retrospective, single-center study based on a large, longitudinal data set obtained from the EHRs of the largest tertiary hospital in Singapore. Study outcomes include adverse events experienced by patients, such as the need for an intensive care unit and inpatient death. With preidentified candidate variables drawn from expert opinions and relevant literature, we will apply an interpretable machine learning–based AutoScore to develop 3 SERT scores. These 3 scores can be used at different times in the ED, that is, on arrival, during ED stay, and at admission. Furthermore, we will compare our novel SERT scores with established clinical scores and previously described black box machine learning models as baselines. Receiver operating characteristic analysis will be conducted on the testing cohorts for performance evaluation. Results The study is currently being conducted. The extracted data indicate approximately 1.8 million ED visits by over 810,000 unique patients. Modelling results are expected to be published in 2022. Conclusions The SERT scoring system proposed in this study will be unique and innovative because of its dynamic nature and modelling transparency. If successfully validated, our proposed solution will establish a standard for data processing and modelling by taking advantage of large-scale EHRs and interpretable machine learning tools. International Registered Report Identifier (IRRID) DERR1-10.2196/34201

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

Cited by 25 publications

References 65 publications

Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews

Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews

Artificial Intelligence for COVID-19 Detection in Medical Imaging—Diagnostic Measures and Wasting—A Systematic Umbrella Review

Leveraging Large-Scale Electronic Health Records and Interpretable Machine Learning for Clinical Decision Making at the Emergency Department: Protocol for System Development and Validation

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