A Prediction Model for High Risk of Positive RT-PCR Test Results in COVID-19 Patients Discharged From Wuhan Leishenshan Hospital, China

Qian, Yawei; Zeng, Guang; Pan, Y.; Liu, Yang; Zhang, Limao; Li, Kun

doi:10.3389/fpubh.2021.778539

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…The final forecasting outcome is the average value of the output results of all decision trees according to Eqn (1). To evaluate the prediction performance of the RF regres-sion model, the prediction accuracy of the RF model is comprehensively evaluated in terms of its precision and stability, and the commonly used average is selected (Liu et al, 2022;Qian et al, 2021). The root mean square error (RMSE) and goodness of fit (R 2 ) are the two parameters used to assess the accuracy of the prediction results.…”

Section: Validation Of the Prediction Modelmentioning

confidence: 99%

Intelligent Prediction of the Frost Resistance of High-Performance Concrete: A Machine Learning Method

Zhang,

Cao,

Xia

et al. 2023

Journal of Civil Engineering and Management

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Frost resistance in very cold areas is an important engineering issue for the durability of concrete, and the efficient and accurate prediction of the frost resistance of concrete is a crucial basis for determining reasonable design mix proportions. For a quick and accurate prediction of the frost resistance of concrete, a Bayesian optimization (BO)-random forest (RF) approach was used to establish a frost resistance prediction model that consists of three phases. A case study of a key national engineering project results show that (1) the RF can be used to effectively screen the factors that influence concrete frost resistance. (2) R2 of BO-RF for the training set and the test set are 0.967 and 0.959, respectively, which are better than those of the other algorithms. (3) Using the test data from the first section of the project for prediction, good results are obtained for the second section. The proposed BO-RF hybrid algorithm can accurately and quickly predict the frost resistance of concrete, and provide a reference basis for intelligent prediction of concrete durability.

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Section: Validation Of the Prediction Modelmentioning

confidence: 99%

Intelligent Prediction of the Frost Resistance of High-Performance Concrete: A Machine Learning Method

Zhang,

Cao,

Xia

et al. 2023

Journal of Civil Engineering and Management

Self Cite

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show abstract

“…When the dataset is unbalanced, the F1-score metric is often applied. Here, it illustrates the effectiveness of the suggested strategy using the F1 score as an assessment indicator [34]. Equation ( 10) is used to obatin the F1-score.…”

Section: 4 F1-scorementioning

confidence: 99%

Cardiac Failure Forecasting Based on Clinical Data Using a Lightweight Machine Learning Metamodel

Mahmud,

Kabir,

Mridha

et al. 2023

Diagnostics

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Accurate prediction of heart failure can help prevent life-threatening situations. Several factors contribute to the risk of heart failure, including underlying heart diseases such as coronary artery disease or heart attack, diabetes, hypertension, obesity, certain medications, and lifestyle habits such as smoking and excessive alcohol intake. Machine learning approaches to predict and detect heart disease hold significant potential for clinical utility but face several challenges in their development and implementation. This research proposes a machine learning metamodel for predicting a patient’s heart failure based on clinical test data. The proposed metamodel was developed based on Random Forest Classifier, Gaussian Naive Bayes, Decision Tree models, and k-Nearest Neighbor as the final estimator. The metamodel is trained and tested utilizing a combined dataset comprising five well-known heart datasets (Statlog Heart, Cleveland, Hungarian, Switzerland, and Long Beach), all sharing 11 standard features. The study shows that the proposed metamodel can predict heart failure more accurately than other machine learning models, with an accuracy of 87%.

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“…In China, the COVID-19 pandemic had been well-controlled ( 16 , 17 ), several factors ( 18 ) including high population density and general susceptibility makes the vast majority of Wuhan's residents with no immunity against SARS-CoV-2 and at continued risk of infection. For preventing the potential re-emergence of COVID-19, the Chinese government is also trying to promote the coverage of COVID-19 vaccination, especially for particular groups including PLWH.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Vaccination Willingness Among People Living With HIV in Wuhan, China

Ming

Xing

et al. 2022

Front. Public Health

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Vaccination is essential to controlling the pandemic of coronavirus disease 2019 (COVID-19). People living with HIV (PLWH) were considered more vulnerable to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection compared with the general population. Therefore, it is urgent to protect PLWH from SARS-CoV-2 infection. For PLWH, vaccine hesitancy could be more common and may compromise vaccine coverage. Our study aimed to investigate the willingness to receive the COVID-19 vaccination among PLWH and associated factors. A cross-sectional online survey was performed among PLWH and the general population from 4 April to 18 April 2021 in Wuhan, China. The multivariable logistic regression was used to analyze associated factors for COVID-19 vaccination willingness among PLWH. A total of 556 PLWH and 570 individuals from the general population were enrolled. The COVID-19 vaccine willingness among PLWH was 60.8%, which was relatively lower than that in the general population (80.9%) (P < 0.001). The results of multivariable analysis indicated that PLWH with comorbidities (OR = 2.07, 95% CI: 1.25–3.45), those who had idea about PLWH would be more serious if they were infected with SARS-CoV-2 (OR = 1.67, 95% CI: 1.11–2.51) and those who thought their antiretroviral therapy (ART) would be affected by COVID-19 epidemic (OR = 2.04, 95% CI: 1.22–3.42) had higher willingness to receive COVID-19 vaccination. PLWH who had a monthly income over 5,000 RMB (OR = 0.64, 95% CI: 0.45–0.92) and had a sex orientation as non-homosexual (OR = 0.67, 95% CI: 0.47–0.96) were associated with lower willingness for COVID-19 vaccination. Our findings showed that the PLWH had a lower willingness for COVID-19 vaccination compared with the general population in Wuhan. Targeted interventions such as health education should be conducted to increase the willingness for COVID-19 vaccination among PLWH, thus enhancing COVID-19 vaccine uptake among PLWH.

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A Prediction Model for High Risk of Positive RT-PCR Test Results in COVID-19 Patients Discharged From Wuhan Leishenshan Hospital, China

Cited by 6 publications

References 19 publications

Intelligent Prediction of the Frost Resistance of High-Performance Concrete: A Machine Learning Method

Intelligent Prediction of the Frost Resistance of High-Performance Concrete: A Machine Learning Method

Cardiac Failure Forecasting Based on Clinical Data Using a Lightweight Machine Learning Metamodel

COVID-19 Vaccination Willingness Among People Living With HIV in Wuhan, China

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