Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making

Pourhomayoun, Mohammad; Shakibi, Mahdi

doi:10.1016/j.smhl.2020.100178

Cited by 186 publications

(166 citation statements)

References 9 publications

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“…Original research focused on predicting progression to severe disease, intensive care admission, ventilator use, or mortality (Table 5 ). Almost half of the studies used data routinely captured by an EHR or obtained through a quick patient history, and explored various classification algorithms (13/30 studies; 43%) 54 – 66 . Nine studies compared the performance of complex ML with simple logistic regression 54 – 62 .…”

Section: Resultsmentioning

confidence: 99%

“…Almost half of the studies used data routinely captured by an EHR or obtained through a quick patient history, and explored various classification algorithms (13/30 studies; 43%) 54 – 66 . Nine studies compared the performance of complex ML with simple logistic regression 54 – 62 . In four studies, logistic regression was found to have similar or better performance 55 , 57 , 58 , 60 , and of three studies that developed a clinical prediction tool, two selected logistic regression as the final model for simplicity and interpretability 55 , 60 .…”

Section: Resultsmentioning

confidence: 99%

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Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases

et al. 2021

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Artificial intelligence (AI) represents a valuable tool that could be widely used to inform clinical and public health decision-making to effectively manage the impacts of a pandemic. The objective of this scoping review was to identify the key use cases for involving AI for pandemic preparedness and response from the peer-reviewed, preprint, and grey literature. The data synthesis had two parts: an in-depth review of studies that leveraged machine learning (ML) techniques and a limited review of studies that applied traditional modeling approaches. ML applications from the in-depth review were categorized into use cases related to public health and clinical practice, and narratively synthesized. One hundred eighty-three articles met the inclusion criteria for the in-depth review. Six key use cases were identified: forecasting infectious disease dynamics and effects of interventions; surveillance and outbreak detection; real-time monitoring of adherence to public health recommendations; real-time detection of influenza-like illness; triage and timely diagnosis of infections; and prognosis of illness and response to treatment. Data sources and types of ML that were useful varied by use case. The search identified 1167 articles that reported on traditional modeling approaches, which highlighted additional areas where ML could be leveraged for improving the accuracy of estimations or projections. Important ML-based solutions have been developed in response to pandemics, and particularly for COVID-19 but few were optimized for practical application early in the pandemic. These findings can support policymakers, clinicians, and other stakeholders in prioritizing research and development to support operationalization of AI for future pandemics.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases

et al. 2021

View full text Add to dashboard Cite

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“…Different ML models have been proposed to predict risk of developing severe complications and mortality (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27). This is important since there are limited resources compared to the increasing number of COVID-19 patients.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Clinical Decision Support System for Early COVID-19 Mortality Prediction

Karthikeyan

Garg

Vinod

et al. 2021

Front. Public Health

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The coronavirus disease 2019 (COVID-19), caused by the virus SARS-CoV-2, is an acute respiratory disease that has been classified as a pandemic by the World Health Organization (WHO). The sudden spike in the number of infections and high mortality rates have put immense pressure on the public healthcare systems. Hence, it is crucial to identify the key factors for mortality prediction to optimize patient treatment strategy. Different routine blood test results are widely available compared to other forms of data like X-rays, CT-scans, and ultrasounds for mortality prediction. This study proposes machine learning (ML) methods based on blood tests data to predict COVID-19 mortality risk. A powerful combination of five features: neutrophils, lymphocytes, lactate dehydrogenase (LDH), high-sensitivity C-reactive protein (hs-CRP), and age helps to predict mortality with 96% accuracy. Various ML models (neural networks, logistic regression, XGBoost, random forests, SVM, and decision trees) have been trained and performance compared to determine the model that achieves consistently high accuracy across the days that span the disease. The best performing method using XGBoost feature importance and neural network classification, predicts with an accuracy of 90% as early as 16 days before the outcome. Robust testing with three cases based on days to outcome confirms the strong predictive performance and practicality of the proposed model. A detailed analysis and identification of trends was performed using these key biomarkers to provide useful insights for intuitive application. This study provide solutions that would help accelerate the decision-making process in healthcare systems for focused medical treatments in an accurate, early, and reliable manner.

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“…Due to the exponential spread of the COVID-19 pandemic, most of the existing medical facilities are overburdened and complete care to all the infected patients may not be possible in some regions. Pourhomayounet al [120] devised an AI technique to aid the medical staff in the decision-making process. Their proposed model predicted the mortality of a patient with an accuracy of 93% from virus nucleic acid information from a dataset of more than 100,000 confirmed patients worldwide.…”

Section: Prognosismentioning

confidence: 99%

“…The patients with higher mortality rates should be given more priority and also be assigned more hospital resources. Many algorithms were unitized and tested, but the neural network approach worked the best performance and accuracy-wise [120]. Multi-tree XGBoost algorithm was proposed by Chowdhury et al [29] for the prediction of mortality based on the dataset of 375 COVID-19 positive patients in China.…”

Section: Prognosismentioning

confidence: 99%

Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends

Shah

Tanwar

et al. 2021

Multimedia Systems

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The COVID-19 pandemic is rapidly spreading across the globe and infected millions of people that take hundreds of thousands of lives. Over the years, the role of Artificial intelligence (AI) has been on the rise as its algorithms are getting more and more accurate and it is thought that its role in strengthening the existing healthcare system will be the most profound. Moreover, the pandemic brought an opportunity to showcase AI and healthcare integration potentials as the current infrastructure worldwide is overwhelmed and crumbling. Due to AI's flexibility and adaptability, it can be used as a tool to tackle COVID-19. Motivated by these facts, in this paper, we surveyed how the AI techniques can handle the COVID-19 pandemic situation and present the merits and demerits of these techniques. This paper presents a comprehensive end-to-end review of all the AI-techniques that can be used to tackle all areas of the pandemic. Further, we systematically discuss the issues of the COVID-19, and based on the literature review, we suggest their potential countermeasures using AI techniques. In the end, we analyze various open research issues and challenges associated with integrating the AI techniques in the COVID-19.

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Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making

Cited by 186 publications

References 9 publications

Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases

Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases

Machine Learning Based Clinical Decision Support System for Early COVID-19 Mortality Prediction

Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends

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