Prediction of death status on the course of treatment in SARS-COV-2 patients with deep learning and machine learning methods

Kivrak, Mehmet; Güldoğan, Emek; Çolak, Cemil

doi:10.1016/j.cmpb.2021.105951

Cited by 34 publications

(14 citation statements)

References 24 publications

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“…In particular, Random Forest models have been used very frequently in prediction analyses, showing high performance with respect to other models 37 – 40 . Recently, they have been employed to compute COVID-19 mortality or to predict the risk of mortality 37 , 41 , 42 . The data used for the analyses are in prevalence based on patients’ physiological conditions, symptoms, demographic information 41 , 42 , population characteristics 43 or blood lab results and clinical data 41 , 44 – 47 .…”

Section: Discussionmentioning

confidence: 99%

The role of European health system characteristics in affecting Covid 19 lethality during the early days of the pandemic

Giancotti

Lopreite

Mauro

et al. 2021

Sci Rep

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This article examines the main factors affecting COVID-19 lethality across 16 European Countries with a focus on the role of health system characteristics during the first phase of the diffusion of the virus. Specifically, we investigate the leading causes of lethality at 10, 20, 30, 40 days in the first hit of the pandemic. Using a random forest regression (ML), with lethality as outcome variable, we show that the percentage of people older than 65 years (with two or more chronic diseases) is the main predictor variable of lethality by COVID-19, followed by the number of hospital intensive care unit beds, investments in healthcare spending compared to GDP, number of nurses and doctors. Moreover, the variable of general practitioners has little but significant predicting quality. These findings contribute to provide evidence for the prediction of lethality caused by COVID-19 in Europe and open the discussion on health policy and management of health care and ICU beds during a severe epidemic.

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Section: Discussionmentioning

confidence: 99%

The role of European health system characteristics in affecting Covid 19 lethality during the early days of the pandemic

Giancotti

Lopreite

Mauro

et al. 2021

Sci Rep

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“…The algorithm has been the source of countless cutting-edge applications, and it has been the driving force behind many of these recent advances. It's been widely used as industrial solutions such as customer churn prediction [9], applicant risk assessment [10], malware detection [11], stock market selection [12], classification of traffic accidents [13], diseases identification [14], and even in predicting the death of patience during SARS-COV-2(Covid-19) treatment [15]. In general, the XGBoost algorithms are the evolution of decision tree algorithms that were improved over time.…”

Section: Fundamental Theoretical Of Xgboostmentioning

confidence: 99%

Comparison Studies Between Machine Learning Optimisation Technique on Predicting Concrete Compressive Strength

Rathakrishnan¹,

Beddu²,

Ahmed³

2021

Preprint

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In this research, a comparison study of the machine learning (ML) optimisation technique to predict the compressive strength of concrete is discussed. In previous studies, researchers focused on identifying the machine learning model by comparing, ensemble, bagging, and fusion methods in predicting the concrete strength. In this research, an ML model hyper-parameter optimisation is used to improve the prediction accuracy and performance of the model. Extreme gradient boosting (XGBoost) is used as the base model to perform the prediction, as the XGBoost has a built-in model ensemble, bagging, and boosting algorithms. Grid Search, Random Search, and Bayesian Optimisation are selected and used to optimise the hyperparameters of the XGBoost model. For this particular prediction study, the optimised models based on Random Search performed better than other optimisation methods. The Random Search optimisation method showed substantial improvements in prediction accuracy, modelling error and computation time.

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“…This has been further complicated by the lack of knowledge pertaining to the factors driving the severity of the SARS-CoV-2 infection. Previous attempts have indicated variable success in predicting the infection prognosis using machine learning and deep learning (interpretable as well as black-box) methods based on the symptom profile, co-morbidities, blood biomarkers, chromosomal-scale length variation, epitope profiling of infected individuals [1] , [2] , [3] , [4] , [5] , [6] . Such efforts are important as they lay the ground for a much-needed thought towards predictive prognosis which may aid in mitigating the potential burden on healthcare system.…”

Section: Introductionmentioning

confidence: 99%

Can machines learn the mutation signatures of SARS-CoV-2 and enable viral-genotype guided predictive prognosis?

Nagpal

Pinna

Pant

et al. 2022

Journal of Molecular Biology

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Prediction of death status on the course of treatment in SARS-COV-2 patients with deep learning and machine learning methods

Cited by 34 publications

References 24 publications

The role of European health system characteristics in affecting Covid 19 lethality during the early days of the pandemic

The role of European health system characteristics in affecting Covid 19 lethality during the early days of the pandemic

Comparison Studies Between Machine Learning Optimisation Technique on Predicting Concrete Compressive Strength

Can machines learn the mutation signatures of SARS-CoV-2 and enable viral-genotype guided predictive prognosis?

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