Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values

Cavallaro, Massimo; Moiz, Haseeb; Keeling, Matthew James; McCarthy, Noel D.

doi:10.1101/2020.12.03.20242941

Cited by 4 publications

(7 citation statements)

References 38 publications

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“…Multivariable modeling was used in a subset of 24 studies of asthma and COVID-19 mortality (13,17,24,29,46,58,74,76,78,82,90,98,105,118,119,124,127,143,146,152,(155)(156)(157)(158), with adjustment for such factors as age, sex, ethnicity/race, education, and various comorbidities. These studies showed meta-odds ratio = 0.82 (95% CI, 0.79-0.85; P , 0.001), albeit with significant heterogeneity (I 2 = 59.1%; 95% CI, 29.7-72.9) (Table E2).…”

Section: Studies That Adjusted Relative Measures Of Association For Confounding Factorsmentioning

confidence: 99%

Asthma in Adult Patients with COVID-19. Prevalence and Risk of Severe Disease

Terry¹,

Heidel²,

Dhand

2021

Am J Respir Crit Care Med

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Rationale: Health outcomes of people with coronavirus disease (COVID-19) range from no symptoms to severe illness and death. Asthma, a common chronic lung disease, has been considered likely to increase the severity of COVID-19, although data addressing this hypothesis have been scarce until very recently.Objectives: To review the epidemiologic literature related to asthma's potential role in COVID-19 severity.Methods: Studies were identified through the PubMed (MEDLINE) and medRxiv (preprint) databases using the search terms "asthma," "SARS-CoV-2" (severe acute respiratory syndrome coronavirus 2), and "COVID-19," and by cross-referencing citations in identified studies that were available in print or online before December 22, 2020.Measurements and Main Results: Asthma prevalence data were obtained from studies of people with COVID-19 and regional health statistics. We identified 150 studies worldwide that allowed us to compare the prevalence of asthma in patients with COVID-19 by region, disease severity, and mortality. The results of our analyses do not provide clear evidence of increased risk of COVID-19 diagnosis, hospitalization, severity, or mortality due to asthma.Conclusions: These findings could provide some reassurance to people with asthma regarding its potential to increase their risk of severe morbidity from COVID-19.

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Section: Studies That Adjusted Relative Measures Of Association For Confounding Factorsmentioning

confidence: 99%

Asthma in Adult Patients with COVID-19. Prevalence and Risk of Severe Disease

Terry¹,

Heidel²,

Dhand

2021

Am J Respir Crit Care Med

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“…They concluded that medical comorbidities are highly associated with mortality, with percentages of 2.56%, 10.3%, 41.0%, and 6% for heart rate problems, respiratory disease, hypertension, and diabetes; the same trend was found in [ 148 , 149 , 150 , 151 , 152 ]. More details about the correlation between comorbidities and severe diseases are available in [ 153 , 154 ].…”

Section: The Study Taxonomymentioning

confidence: 99%

Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic

et al. 2021

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Since December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artificial intelligence (AI) in this context is difficult to ignore. AI companies have been racing to develop innovative tools that contribute to arm the world against this pandemic and minimize the disruption that it may cause. The main objective of this study is to survey the decisive role of AI as a technology used to fight against the COVID-19 pandemic. Five significant applications of AI for COVID-19 were found, including (1) COVID-19 diagnosis using various data types (e.g., images, sound, and text); (2) estimation of the possible future spread of the disease based on the current confirmed cases; (3) association between COVID-19 infection and patient characteristics; (4) vaccine development and drug interaction; and (5) development of supporting applications. This study also introduces a comparison between current COVID-19 datasets. Based on the limitations of the current literature, this review highlights the open research challenges that could inspire the future application of AI in COVID-19.

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“…Feature occlusion and ablation [28]- [31], [143], [144] SHAP feature importance [32]- [38], [96], [98], [102], [115]- [122], [134] Local interpretable model-agnostic explanations (LIME) [38]- [42] Activation-based Activation maximization [43] Class activation maps (CAM) [44], [45] Gradient-based…”

Section: Perturbation-basedmentioning

confidence: 99%

“…Morphology-based Maximum entropy threshold [92]- [94] Context-based Mult-scale attention [91] Saliency Analysis [52] Network dissection [95] [136], [147]- [149]. In addition to web-based applications, visualizing sample clusters [61], [100] and feature importance metrics [31], [65], [71], [98], [117], [121], [122], [134], [135], [150] can offer users without expertise in data analysis an option of understanding the decision-making process of otherwise obscure models.…”

Section: Perturbation-basedmentioning

confidence: 99%

“…A very common approach to generating easily interpretable models is to optimize a decision tree approach that can describe the decision making process using available features [31], [96], [98], [100]- [102], [115]- [122]. This approach is also similar to commonly used clinical guidelines to generate fast and consistent metrics for patient triage and management [151], [152].…”

Section: Perturbation-basedmentioning

confidence: 99%

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Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review

Giuste¹,

Shi²,

Zhu³

et al. 2021

Preprint

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Despite the myriad peer-reviewed papers demonstrating novel Artificial Intelligence (AI)-based solutions to COVID-19 challenges during the pandemic, few have made significant clinical impact. The impact of artificial intelligence during the COVID-19 pandemic was greatly limited by lack of model transparency. This systematic review examines the use of Explainable Artificial Intelligence (XAI) during the pandemic and how its use could overcome barriers to real-world success. We find that successful use of XAI can improve model performance, instill trust in the end-user, and provide the value needed to affect user decision-making. We introduce the reader to common XAI techniques, their utility, and specific examples of their application. Evaluation of XAI results is also discussed as an important step to maximize the value of AI-based clinical decision support systems. We illustrate the classical, modern, and potential future trends of XAI to elucidate the evolution of novel XAI techniques. Finally, we provide a checklist of suggestions during the experimental design process supported by recent publications. Common challenges during the implementation of AI solutions are also addressed with specific examples of potential solutions. We hope this review may serve as a guide to improve the clinical impact of future AI-based solutions.

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Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values

Cited by 4 publications

References 38 publications

Asthma in Adult Patients with COVID-19. Prevalence and Risk of Severe Disease

Asthma in Adult Patients with COVID-19. Prevalence and Risk of Severe Disease

Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic

Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review

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