Mortality Prediction of COVID-19 Pandemic Using Artificial Intelligence

Dwivedi, Abhishek; Upadhyay, Ashutosh; Pal, Anita

doi:10.1109/icacccn51052.2020.9362740

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…AI technology has shown promising potential for outcome prediction, pattern recognition, and diagnostic classification to support clinicians in decision-making during diagnosis and treatment. 4 , 7 , 13 – 15 Different types of innovative AI technologies for clinical-decision support have been developed 4 , 16 and tested in clinical trials, for example, in sepsis care, 17 – 21 cardiovascular care, 22 – 24 and COVID-19 care 25 , 26 to predict stages of disease, assess prognosis, assist decision-making, and predict mortality risk. 19 – 22 , 25 , 27 , 28 One potentially effective use of predictive analytics in the emergency department is to support decisions on which patient should be admitted to a hospital ward and which patient could be safely discharged or displaced, optimizing the use of healthcare recourses and the quality of healthcare.…”

Section: Introductionmentioning

confidence: 99%

“… 4 , 7 , 13 – 15 Different types of innovative AI technologies for clinical-decision support have been developed 4 , 16 and tested in clinical trials, for example, in sepsis care, 17 – 21 cardiovascular care, 22 – 24 and COVID-19 care 25 , 26 to predict stages of disease, assess prognosis, assist decision-making, and predict mortality risk. 19 – 22 , 25 , 27 , 28 One potentially effective use of predictive analytics in the emergency department is to support decisions on which patient should be admitted to a hospital ward and which patient could be safely discharged or displaced, optimizing the use of healthcare recourses and the quality of healthcare. 8 , 20 , 28 – 30 Such use of AI technology would transform current practice in the emergency department and would address current constraints with a heavy workload and stress on healthcare professionals.…”

Section: Introductionmentioning

confidence: 99%

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Ethical considerations in implementing AI for mortality prediction in the emergency department: Linking theory and practice

Petersson,

Vincent,

Svedberg

et al. 2023

DIGITAL HEALTH

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Background Artificial intelligence (AI) is predicted to be a solution for improving healthcare, increasing efficiency, and saving time and recourses. A lack of ethical principles for the use of AI in practice has been highlighted by several stakeholders due to the recent attention given to it. Research has shown an urgent need for more knowledge regarding the ethical implications of AI applications in healthcare. However, fundamental ethical principles may not be sufficient to describe ethical concerns associated with implementing AI applications. Objective The aim of this study is twofold, (1) to use the implementation of AI applications to predict patient mortality in emergency departments as a setting to explore healthcare professionals’ perspectives on ethical issues in relation to ethical principles and (2) to develop a model to guide ethical considerations in AI implementation in healthcare based on ethical theory. Methods Semi-structured interviews were conducted with 18 participants. The abductive approach used to analyze the empirical data consisted of four steps alternating between inductive and deductive analyses. Results Our findings provide an ethical model demonstrating the need to address six ethical principles (autonomy, beneficence, non-maleficence, justice, explicability, and professional governance) in relation to ethical theories defined as virtue, deontology, and consequentialism when AI applications are to be implemented in clinical practice. Conclusions Ethical aspects of AI applications are broader than the prima facie principles of medical ethics and the principle of explicability. Ethical aspects thus need to be viewed from a broader perspective to cover different situations that healthcare professionals, in general, and physicians, in particular, may face when using AI applications in clinical practice.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ethical considerations in implementing AI for mortality prediction in the emergency department: Linking theory and practice

Petersson,

Vincent,

Svedberg

et al. 2023

DIGITAL HEALTH

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Artificial Intelligence for Epidemiology COVID-19: Quick Assessment

Priya¹,

Narmadha²,

Chatterjee³

2023

Medical Virology: From Pathogenesis to Disease Control

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Mortality Prediction in COVID‐19 Using Time Series and Machine Learning Techniques

Akter,

Hossain,

Ullah

et al. 2024

Computational and Mathematical Methods

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Predicting mortality in COVID‐19 is one of the most significant and difficult tasks at hand. This study compares time series and machine learning methods, including support vector machines (SVMs) and neural networks (NNs), to forecast the mortality rate in seven countries: the United States, India, Brazil, Russia, France, China, and Bangladesh. Data were gathered between December 31, 2019, when COVID‐19 began, and March 31, 2021. The study used 457 observations with 4 variables: daily confirmed cases, daily deaths, daily mortality rate, and date. To predict the death rate in the seven countries that were chosen, the data were analyzed using time series analysis and machine learning techniques. Models were compared to obtain more accurate mortality predictions. The autoregressive integrated moving average (ARIMA) model with the lowest AIC value for each nation is found through time series analysis. By increasing the hidden layer and applying machine learning techniques, the NN model for each country is chosen, and the optimal model is determined by determining the model with the lowest error value. Additionally, SVM analyzes every country and calculates its R2 and root‐mean‐square error (RMSE). The lowest RMSE value is used to compare all of the time series and machine learning models. According to the comparison table, SVM provides a more accurate model to predict the mortality rate of the seven countries, with the lowest RMSE value. During the study period, mortality rates increased in Brazil and Russia and decreased in the United States, India, France, China, and Bangladesh, according to the comparison value of RMSE in this study. Furthermore, this paper shows that SVM outperforms all other models in terms of performance. According to the author’s analysis of the data, SVM is a machine learning technique that can be used to accurately predict mortality in a pandemic scenario.

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Mortality Prediction of COVID-19 Pandemic Using Artificial Intelligence

Cited by 3 publications

References 12 publications

Ethical considerations in implementing AI for mortality prediction in the emergency department: Linking theory and practice

Ethical considerations in implementing AI for mortality prediction in the emergency department: Linking theory and practice

Artificial Intelligence for Epidemiology COVID-19: Quick Assessment

Mortality Prediction in COVID‐19 Using Time Series and Machine Learning Techniques

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