Research on Influencing Factors and Classification of Patients With Mild and Severe COVID-19 Symptoms

ObjectiveTo analyze the epidemiological history, clinical symptoms, laboratory testing parameters of patients with mild and severe COVID-19 infection, and provide a reference for timely judgment of changes in the patients’ conditions and the formulation of epidemic prevention and control strategies.MethodsA retrospective study was conducted in this research, a total of 90 patients with COVID-19 infection who received treatment from January 21 to March 31, 2020 in the Ninth People’s Hospital of Dongguan City were selected as study subject. We analyzed the clinical characteristics of laboratory-confirmed patients with COVID-19, used the oversampling method (SMOTE) to solve the imbalance of categories, and established Lasso-logistic regression and random forest models.ResultsAmong the 90 confirmed COVID-19 cases, 79 were mild and 11 were severe. The average age of the patients was 36.1 years old, including 49 males and 41 females. The average age of severe patients is significantly older than that of mild patients (53.2 years old vs 33.7 years old). The average time from illness onset to hospital admission was 4.1 days and the average actual hospital stay was 18.7 days, both of these time actors were longer for severe patients than for mild patients. Forty-eight of the 90 patients (53.3%) had family cluster infections, which was similar among mild and severe patients. Comorbidities of underlying diseases were more common in severe patients, including hypertension, diabetes and other diseases. The most common symptom was cough [45 (50%)], followed by fever [43 (47.8%)], headache [7 (7.8%)], vomiting [3 (3.3%)], diarrhea [3 (3.3%)], and dyspnea [1 (1.1%)]. The laboratory findings of patients also included leukopenia [13(14.4%)] and lymphopenia (17.8%). Severe patients had a low level of creatine kinase (median 40.9) and a high level of D-dimer. The median NLR of severe patients was 2.82, which was higher than that of mild patients. Logistic regression showed that age, phosphocreatine kinase, procalcitonin, the lymphocyte count of the patient on admission, cough, fatigue, and pharynx dryness were independent predictors of COVID-19 severity. The classification of random forest was predicted and the importance of each variable was displayed. The variable importance of random forest indicates that age, D-dimer, NLR (neutrophil to lymphocyte ratio) and other top-ranked variables are risk factors.ConclusionThe clinical symptoms of COVID-19 patients are non-specific and complicated. Age and the time from onset to admission are important factors that determine the severity of the patient’s condition. Patients with mild illness should be closely monitored to identify those who may become severe. Variables such as age and creatine phosphate kinase selected by logistic regression can be used as important indicators to assess the disease severity of COVID-19 patients. The importance of variables in the random forest further complements the variable feature information.

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Insect bite hypersensitivity in Belgian warmblood horses: prevalence and risk factors

Vlaams Diergeneeskundig Tijdschrift ◽

10.21825/vdt.v83i5.16636 ◽

2014 ◽

Vol 83 (5) ◽

pp. 240-249

Author(s):

L. M. Peeters ◽

S. Janssens ◽

A. Coussé ◽

N. Buys

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Model Selection ◽

Allergic Reaction ◽

Clinical Symptoms ◽

The Self ◽

Face To Face ◽

Regression Framework ◽

Insect Bite Hypersensitivity

Insect bite hypersensitivity (IBH) is an allergic reaction to the bites of certain Culicoides spp. or other insects. In this study, risk factors for IBH in Belgian warmblood horses stabled or grazing in Flanders (Belgium) were investigated. IBH records (n=3409) were collected in 2009 and 2011 using a questionnaire and face-to-face interviews. The classification of IBH-affected versus unaffected horses was based on the owner’s statement, and the reported IBH lifetime prevalence was 10%. Thirty eight percent of IBH affected horses had no clinical symptoms at the time of questioning. When only the presence or absence of clinical symptoms at the time of questioning was taken into account, the prevalence of IBH symptoms was 6.2%. Seventy percent of IBH-affected horses were treated with IBH measures to reduce clinical symptoms. Model selection was based on backwards elimination in a logistic regression framework starting with 17 factors. The age of the horse, vegetation of surrounding pasture and stud size were found to be significantly associated with the self-reported IBH status.

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Comparison of Random Forest, Logistic Regression, and MultilayerPerceptron Methods on Classification of Bank Customer Account Closure

Indonesian Journal of Applied Statistics ◽

10.13057/ijas.v4i1.41461 ◽

2021 ◽

Vol 4 (1) ◽

pp. 14

Author(s):

Husna Afanyn Khoirunissa ◽

Amanda Rizky Widyaningrum ◽

Annisa Priliya Ayu Maharani

Keyword(s):

Data Mining ◽

Logistic Regression ◽

Feature Selection ◽

Random Forest ◽

Multilayer Perceptron ◽

Cross Validation ◽

Early Stage ◽

Bank Account ◽

Credit Score

<p>The Bank is a business entity that is dealing with money, accepting deposits from customers, providing funds for each withdrawal, billing checks on the customer's orders, giving credit and or embedding the excess deposits until required for repayment. The purpose of this research is to determine the influence of age, gender, country, customer credit score, number of bank products used by the customer, and the activation of the bank members in the decision to choose to continue using the bank account that he has retained or closed the bank account. The data in this research used 10,000 respondents originating from France, Spain, and Germany. The method used is data mining with early stage preprocessing to clean data from outlier and missing value and feature selection to select important attributes. Then perform the classification using three methods, which are Random Forest, Logistic Regression, and Multilayer Perceptron. The results of this research showed that the model with Multilayer Perceptron method with 10 folds Cross Validation is the best model with 85.5373% accuracy.</p><strong>Keywords:</strong> bank customer, random forest, logistic regression, multilayer perceptron

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Prognostic value of neutrophil-to-lymphocyte ratio, lactate dehydrogenase, D-dimer and CT score in patients with COVID-19

10.21203/rs.3.rs-38585/v1 ◽

2020 ◽

Author(s):

Yu-Qing Cai ◽

Hui-Qing Zeng ◽

Xiao-Bin Zhang ◽

Xiao-Jie Wei ◽

Lan Hu ◽

...

Keyword(s):

Logistic Regression ◽

Lactate Dehydrogenase ◽

Independent Predictor ◽

Neutrophil To Lymphocyte Ratio ◽

Multivariate Logistic Regression ◽

Laboratory Findings ◽

Lymphocyte Ratio ◽

Severe Group ◽

High Level ◽

D Dimer

Abstract Background To explore the significance of neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase (LDH), D-dimer and CT score in evaluating the severity and prognosis of coronavirus disease – 2019 (COVID-19). Methods Patients with laboratory confirmed COVID-19 were retrospectively enrolled. The baseline data, laboratory findings, chest computed tomography (CT) results evaluating by CT score on admission, and clinical outcomes were collected and compared. The logistic regression was used to assess the independent relationship between the baseline level of four indicators (NLR, LDH, D-dimer, CT score) and the severity of COVID-19. Results Among 432 patients, 125 (28.94%) cases were divided into severe group, the remaining (n = 307, 71.06%) were in non-severe group. In multivariate logistic regression, high level of NLR, LDH were independent predictor of the severe group in COVID-19 (OR = 2.163; 95%CI = 1.162–4.026; p = 0.015 for NLR > 3.82; OR = 2.298; 95%CI = 1.327–3.979; p = 0.003 for LDH > 246U/L). Combining NLR > 3.82 and LDH > 246U/L increased the sensitivity of diagnosis in severe patients (NLR > 3.82 [50.40%] vs. Combined diagnosis [72.80%]; p = 0.0007; LDH > 246 [59.2%] vs. Combined diagnosis [72.80%]; p < 0.0001). Conclusions High levels of NLR and LDH in serum have potential value in the early identification of severe patients with COVID-19. The combination of LDH and NLR can improve the sensitivity of diagnosis.

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Clinical Characteristics and Predictors of Mortality for Young Adults with Severe COVID-19: A Retrospective Study

10.21203/rs.3.rs-31386/v1 ◽

2020 ◽

Author(s):

Yanjiao Lu ◽

Zhenli Huang ◽

Meijia Wang ◽

Kun Tang ◽

Shanshan Wang ◽

...

Keyword(s):

Young Adults ◽

Logistic Regression ◽

Elevated Level ◽

Clinical Characteristics ◽

Troponin I ◽

Multivariate Logistic Regression Analysis ◽

Laboratory Findings ◽

Predictors Of Mortality ◽

Hs Crp ◽

D Dimer

Abstract Background and objective: Little is yet known whether pathogenesis of COVID-19 is different between young and elder patients. Our study aimed to investigate the clinical characteristics and provide predictors of mortality for young adults with severe COVID-19.Methods: A total of 77 young adults with confirmed severe COVID-19 were recruited retrospectively at Tongji Hospital. Clinical characteristics, laboratory findings, treatment and outcomes were obtained from electronic medical records. The prognostic effects of variables were analyzed using logistic regression model.Results: In this retrospective cohort, non-survivors showed higher incidence of dyspnea and co-existing laboratory abnormalities, compared with young survivals in severe COVID-19. Multivariate logistic regression analysis showed that lymphopenia, elevated level of d-dimer, hypersensitive cardiac troponin I (hs-CTnI) and high sensitivity C-reactive protein (hs-CRP) were independent predictors of mortality in young adults with severe COVID-19. Further analysis showed that severely young adults with two or more factors abnormalities above would be more prone to death. The similar predictive effect of above four factors had been observed in all-age patients with severe COVID-19.Conclusion: Lymphopenia, elevated level of d-dimer, hs-CTnI and hs-CRP predicted clinical outcomes of young adults with severe COVID-19.

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Associated risk factors with disease severity and antiviral drug therapy in patients with COVID-19

10.21203/rs.3.rs-70914/v2 ◽

2020 ◽

Author(s):

Xiaowei Gong ◽

Xianfeng Guo ◽

Shiwei Kang ◽

Yan Li ◽

Haixiang Gao ◽

...

Keyword(s):

Risk Factors ◽

Clinical Symptoms ◽

Discharge Rate ◽

Antiviral Drug ◽

Oxygenation Index ◽

Multivariable Logistic Regression Analysis ◽

Common Symptom ◽

Clinical Benefits ◽

Novel Coronavirus ◽

D Dimer

Abstract Background: Due to the latent onset of novel coronavirus disease 2019 (COVID-19), it is important to identify patients with increased probabilities for disease progression early in order to implement timely medical strategies. This study aimed to identify the factors associated with increased COVID-19 severity and evaluate the current antiviral drugs, especially in severe patients. Methods: This was a retrospective observational study performed at the No. 7 Hospital of Wuhan (Wuhan, China) with hospitalized patients confirmed with COVID-19 from January 11 to March 13, 2020. Multivariable logistic regression analysis was used to identify the associated factors of severe COVID. Treatments of antivirus drugs were collected and evaluated.Results: Of the 550 patients, 292 (53.1%) were female and 277 (50.4%) were >60 years old. The most common symptom was fever (n=372, 67.7%), followed by dry cough (n=257, 46.7%), and dyspnea (n=237, 43.1%), and fatigue (n=224, 40.7%). Among the severe patients, 20.2% required invasive ventilator support and 18.0% required non-invasive ventilator. The identified risk factors for severe cases were: age ≥60 years (odds ratio (OR) =3.02, 95% confidence interval (CI): 1.13-8.08, P=0.028), D-dimer >0.243 μg/ml (OR=2.734, 95%CI: 1.012-7.387, P=0.047), and low oxygenation index (OR=0.984, 95%CI: 0.980-0.989, P<0.001). In severe cases, the benefits (relief of clinical symptoms, clinical outcome, and discharge rate) of arbidol alone was 73.3%, which was better than ribavirin (7/17, 41.2%, P=0.029).Conclusions: Age >60 years, D-dimer >0.243 µg/ml, and lower oxygenation index were associated with severe COVID-19. Arbidol might provide more clinical benefits in treating patients with severe COVID-19 compared with ribavirin.

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Clinical Characteristics and Predictors of Mortality in Young Adults with Severe COVID-19. A Retrospective Observational Study

10.21203/rs.3.rs-31386/v3 ◽

2021 ◽

Author(s):

Yanjiao Lu ◽

Zhenli Huang ◽

Meijia Wang ◽

Kun Tang ◽

Shanshan Wang ◽

...

Keyword(s):

Young Adults ◽

Logistic Regression ◽

Elevated Level ◽

Clinical Characteristics ◽

Troponin I ◽

Multivariate Logistic Regression Analysis ◽

Laboratory Findings ◽

Predictors Of Mortality ◽

Hs Crp ◽

D Dimer

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Image Classification of Tourist Attractions with K-Nearest Neighbor, Logistic Regression, Random Forest, and Support Vector Machine

International Journal on Advanced Science Engineering and Information Technology ◽

10.18517/ijaseit.10.6.9098 ◽

2020 ◽

Vol 10 (6) ◽

pp. 2207

Author(s):

Herry Sujaini

Keyword(s):

Support Vector Machine ◽

Logistic Regression ◽

Random Forest ◽

Image Classification ◽

Nearest Neighbor ◽

Support Vector ◽

K Nearest Neighbor ◽

Tourist Attractions

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Classification of HIV-1 protease crystal structures using Random Forest, linear discriminant analysis and logistic regression

2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology ◽

10.1109/cibcb.2010.5510465 ◽

2010 ◽

Author(s):

Gene M. Ko ◽

A. Srinivas Reddy ◽

Sunil Kumar ◽

Barbara A. Bailey ◽

Rajni Garg

Keyword(s):

Logistic Regression ◽

Discriminant Analysis ◽

Random Forest ◽

Linear Discriminant Analysis ◽

Crystal Structures ◽

Linear Discriminant ◽

Hiv 1

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Statistical and machine learning models for classification of human wear and delivery days in accelerometry data

10.1101/2020.12.31.424867 ◽

2021 ◽

Author(s):

Ryan Moore ◽

Kristin R. Archer ◽

Leena Choi

Keyword(s):

Neural Network ◽

Machine Learning ◽

Logistic Regression ◽

Random Forest ◽

Human Activity ◽

Recurrent Neural Network ◽

Learning Models ◽

Learning Context ◽

Machine Learning Models

AbstractPurposeAccelerometers are increasingly utilized in healthcare research to assess human activity. Accelerometry data are often collected by mailing accelerometers to participants, who wear the accelerometers to collect data on their activity. The devices are then mailed back to the laboratory for analysis. We develop models to classify days in accelerometry data as activity from actual human wear or the delivery process. These models can be used to automate the cleaning of accelerometry datasets that are adulterated with activity from delivery.MethodsFor the classification of delivery days in accelerometry data, we developed statistical and machine learning models in a supervised learning context using a large human activity and delivery labeled accelerometry dataset. We extracted several features, which were included to develop random forest, logistic regression, mixed effects regression, and multilayer perceptron models, while convolutional neural network, recurrent neural network, and hybrid convolutional recurrent neural network models were developed without feature extraction. Model performances were assessed using Monte Carlo cross-validation.ResultsWe found that a hybrid convolutional recurrent neural network performed best in the classification task with an F1 score of 0.960 but simpler models such as logistic regression and random forest also had excellent performance with F1 scores of 0.951 and 0.957, respectively.ConclusionThe models developed in this study can be used to classify days in accelerometry data as either human or delivery activity. An analyst can weigh the larger computational cost and greater performance of the convolutional recurrent neural network against the faster but slightly less powerful random forest or logistic regression. The best performing models for classification of delivery data are publicly available on the open source R package, PhysicalActivity.

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Clinical characteristics and predictors of mortality in young adults with severe COVID-19: a retrospective observational study

Annals of Clinical Microbiology and Antimicrobials ◽

10.1186/s12941-020-00412-9 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Yanjiao Lu ◽

Zhenli Huang ◽

Meijia Wang ◽

Kun Tang ◽

Shanshan Wang ◽

...

Keyword(s):

Young Adults ◽

Logistic Regression ◽

Elevated Level ◽

Clinical Characteristics ◽

Troponin I ◽

Multivariate Logistic Regression Analysis ◽

Laboratory Findings ◽

Predictors Of Mortality ◽

Hs Crp ◽

D Dimer

Abstract Background and objective Little is yet known whether pathogenesis of COVID-19 is different between young and elder patients. Our study aimed to investigate the clinical characteristics and provide predictors of mortality for young adults with severe COVID-19. Methods A total of 77 young adults with confirmed severe COVID-19 were recruited retrospectively at Tongji Hospital. Clinical characteristics, laboratory findings, treatment and outcomes were obtained from electronic medical records. The prognostic effects of variables were analyzed using logistic regression model. Results In this retrospective cohort, non-survivors showed higher incidence of dyspnea and co-existing laboratory abnormalities, compared with young survivals in severe COVID-19. Multivariate logistic regression analysis showed that lymphopenia, elevated level of d-dimer, hypersensitive cardiac troponin I (hs-CTnI) and high sensitivity C-reactive protein (hs-CRP) were independent predictors of mortality in young adults with severe COVID-19. Further analysis showed that severely young adults with two or more factors abnormalities above would be more prone to death. The similar predictive effect of above four factors had been observed in all-age patients with severe COVID-19. Conclusion Lymphopenia, elevated level of d-dimer, hs-CTnI and hs-CRP predicted clinical outcomes of young adults with severe COVID-19.

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