Multi-class Logistic Regression Model Research Articles

Analysis of Factors Influencing EV Power Battery Recycling Channel Selection: A Study Based on TPB and Multi Class Logistic Regression

New energy electric vehicle (EV) have become the mainstream worldwide, and the power battery recycling industry is facing new opportunities and challenges. This article compares and analyzes the advantages and disadvantages of existing power battery recycling channels. Through a questionnaire survey, a multi class logistic regression model is used to study the impact and correlation of users' gender, age, occupational category, and education level on their choice of recycling channels. The results indicate that there is a positive correlation between education level and understanding of power battery channels. The higher the education level, the more concerned the group is about environmental protection compared to convenience. Finally, based on the TPB, the impact of seven factors on users' choice of recycling channels and corresponding measures were summarized to help the enterprises understand users' inner thoughts, facilitate the establishment of a more comprehensive recycling mechanism, and improve the revenue of power battery recycling.

Development of an image-based Random Forest classifier for prediction of surgery duration of laparoscopic sigmoid resections

PurposeSigmoid diverticulitis is a disease with a high socioeconomic burden, accounting for a high number of left-sided colonic resections worldwide. Modern surgical scheduling relies on accurate prediction of operation times to enhance patient care and optimize healthcare resources. This study aims to develop a predictive model for surgery duration in laparoscopic sigmoid resections, based on preoperative CT biometric and demographic patient data.MethodsThis retrospective single-center cohort study included 85 patients who underwent laparoscopic sigmoid resection for diverticular disease. Potentially relevant procedure-specific anatomical parameters recommended by a surgical expert were measured in preoperative CT imaging. After random split into training and test set (75% / 25%) multiclass logistic regression was performed and a Random Forest classifier was trained on CT imaging parameters, patient age, and sex in the training cohort to predict categorized surgery duration. The models were evaluated in the test cohort using established performance metrics including receiver operating characteristics area under the curve (AUROC).ResultsThe Random Forest model achieved a good average AUROC of 0.78. It allowed a very good prediction of long (AUROC = 0.89; specificity 0.71; sensitivity 1.0) and short (AUROC = 0.81; specificity 0.77; sensitivity 0.56) procedures. It clearly outperformed the multiclass logistic regression model (AUROC: average = 0.33; short = 0.31; long = 0.22).ConclusionA Random Forest classifier trained on demographic and CT imaging biometric patient data could predict procedure duration outliers of laparoscopic sigmoid resections. Pending validation in a multicenter study, this approach could potentially improve procedure scheduling in visceral surgery and be scaled to other procedures.

The reasons for not returning to work and health-related quality of life among young and middle-aged patients with stroke: A cross-sectional study.

This study aimed to explore the reasons and influencing factors for non-return to work (non-RTW) within 1 year among young and middle-aged patients with stroke and to assess their health-related quality of life (HRQoL) at 1 year across different reasons. The study was conducted as a telephone-based cross-sectional survey. Seven hundred eighty-nine young and middle-aged patients with stroke aged between 18 and 54 years for men and 18 and 49 years for women in the electronic medical system were included. Data collection included demographic characteristics, socioeconomic status, behavioral habits, history of chronic diseases, work status, reasons for non-RTW, and HRQoL. Of 789 patients, 435 (55.1%) (mean [SD] age, 47.7 [7.8] years) did not return to work within 1 year after stroke. Among the patients who did not RTW, 58.9% were unable to work, 9.7% retired early, 11.03% became full-time homemakers or were unemployed, and 20.5% were reluctant to work. The disordered multiclass logistic regression model showed that the factors influencing the reasons for non-RTW included age, gender, education, income, health insurance, diabetes comorbidity, ability to perform activities of daily living, and mobility of the right upper extremity. Furthermore, patients who were unable to work had significantly lower HRQoL compared to those who had RTW, followed by those who retired early. More than half did not RTW within 1 year in our study. The results will help inform future research to identify interventions to promote RTW and improve HRQoL for young and middle-aged patients with stroke.

Analysis of the Harmfulness of Abnormal Riding Behaviors of Electric Bicycles Based on Improved Multiclass Logistic Regression Model

To analyze the harmfulness of abnormal riding behaviors of electric bicycles in-depth, the research focuses on the 2022 electric bicycle accident data in a specific city in China. Based on an improved multiclass logistic regression model, the relationship between different abnormal riding behaviors and the severity of electric bicycle traffic accidents is explored. Firstly, the severity of accidents is categorized into three levels as the dependent variable, while driver attributes and various hazardous driving behaviors serve as independent variables to construct the multiclass logistic regression model. Secondly, the model is optimized by eliminating irrelevant independent variables and improving the link function. Finally, the harmfulness of abnormal riding behaviors of electric bicycles is analyzed based on the results of the regression model. The results indicate that eight factors significantly influence the dependent variable, with three factors, including driving under the influence of alcohol, being more likely to lead to fatal accidents, requiring focused attention for intervention and regulation.

Classification of neurologic outcomes from medical notes using natural language processing

Neurologic disability level at hospital discharge is an important outcome in many clinical research studies. Outside of clinical trials, neurologic outcomes must typically be extracted by labor intensive manual review of clinical notes in the electronic health record (EHR). To overcome this challenge, we set out to develop a natural language processing (NLP) approach that automatically reads clinical notes to determine neurologic outcomes, to make it possible to conduct larger scale neurologic outcomes studies. We obtained 7314 notes from 3632 patients hospitalized at two large Boston hospitals between January 2012 and June 2020, including discharge summaries (3485), occupational therapy (1472) and physical therapy (2357) notes. Fourteen clinical experts reviewed notes to assign scores on the Glasgow Outcome Scale (GOS) with 4 classes, namely ‘good recovery’, ‘moderate disability’, ‘severe disability’, and ‘death’ and on the Modified Rankin Scale (mRS), with 7 classes, namely ‘no symptoms’, ‘no significant disability’, ‘slight disability’, ‘moderate disability’, ‘moderately severe disability’, ‘severe disability’, and ‘death’. For 428 patients’ notes, 2 experts scored the cases generating interrater reliability estimates for GOS and mRS. After preprocessing and extracting features from the notes, we trained a multiclass logistic regression model using LASSO regularization and 5-fold cross validation for hyperparameter tuning. The model performed well on the test set, achieving a micro average area under the receiver operating characteristic and F-score of 0.94 (95% CI 0.93–0.95) and 0.77 (0.75–0.80) for GOS, and 0.90 (0.89–0.91) and 0.59 (0.57–0.62) for mRS, respectively. Our work demonstrates that an NLP algorithm can accurately assign neurologic outcomes based on free text clinical notes. This algorithm increases the scale of research on neurological outcomes that is possible with EHR data.

A Framework for Harmonization of Radiomics Data for Multicenter Studies and Clinical Trials

Variability in computed tomography images intrinsic to individual scanners limits the application of radiomics in clinical and research settings. The development of reproducible and generalizable radiomics-based models to assess lesions requires harmonization of data. The purpose of this study was to develop, test, and analyze the efficacy of a radiomics data harmonization model. Radiomic features from biopsy-proven untreated hepatic metastasis (N = 380) acquired from 167 unique patients with pancreatic, colon, and breast cancers were analyzed. Radiomic features from volume-match 551 samples of normal liver tissue and 188 hepatic cysts were included as references. A novel linear mixed effect model was used to identify effects associated with lesion size, tissue type, and scanner model. Six separate machine learning models were then used to test the effectiveness of radiomic feature harmonization using multivariate analysis. Proposed model identifies and removes scanner-associated effects while preserving cancer-specific functional dependence of radiomic features on the tumor size. Data harmonization improves the performance of classification models by reducing the scanner-associated variability. For example, the multiclass logistic regression model, LogitBoost, demonstrated the improvement in sensitivity in the range from 15% to 40% for each type of liver metastasis, whereas the overall model accuracy and the kappa coefficient increased by 5% and 8% accordingly. The model removed scanner-associated effects while preserving cancer-specific functional dependence of radiomic features.

A stochastic conditional gradient algorithm for decentralized online convex optimization

We study in this paper several problems at the intersection of decentralized optimization and online learning. Decentralized optimization plays a vital role in machine learning and has recently garnered much attention due to its inherent advantage in handling edge computations. Many decentralized optimization algorithms, both projection and projection-free algorithms with theoretical guarantees, have been proposed in the literature, focusing mainly on offline settings. However, for most real-world machine learning problems, the data is often revealed online, for example, in the case of recommender systems, image/video processing, and stock portfolio management. Therefore, in this work, we study decentralized optimization within the framework of online settings with constraints imposed on the optimization solutions (e.g., sparsity or low rank of matrices). More specifically, we consider the problem of optimizing an aggregate of convex loss functions that arrive over time such that their components are distributed over a connected network. We present a consensus-based online decentralized Frank-Wolfe algorithm that uses stochastic gradient estimates, which achieves an asymptotically tight regret guarantee of O(T) where T is a given time horizon. Furthermore, we demonstrate the performance of this algorithm for optimizing the online multiclass logistic regression model on real-world standard image datasets (MNIST, CIFAR10) by comparing with centralized online algorithms. We achieve better regret bounds than the previously best-known decentralized constrained online algorithms.

A Novel Application of Non-Negative Matrix Factorization to the Prediction of the Health Status of Undocumented Immigrants.

Introduction: Undocumented immigrants (UIs) in the United States are less likely to be able to afford health insurance. As a result, UIs often lack family doctors and are rarely involved in annual screening programs, which makes estimating their health status remarkably challenging. This is especially true if the laboratory results from limited screening programs fail to provide sufficient clinical information.Methods: To address this issue, we have developed a machine learning model based on the non-negative matrix factorization technique. The data set we used for model training and testing was obtained from the 2004 cost-free hepatitis B screening program at the Omni Health Center located in Plano, Texas. Total 300 people were involved, with 199 identified as UIs.Results: People in the UIs group have higher cholesterol (219.6 mg/dL, p=0.038) and triglycerides (173.2 mg/dL, p=0.03) level. They also have a lower hepatitis B vaccination rate (38%, p=0.0247). No significant difference in hepatitis B(+) was found (p=0.8823). Using 16 individual clinical measurements as training features, our newly developed model has a 67.56% accuracy in predicting the ratio of cholesterol to high-density lipoprotein; in addition, this newly developed model performs 9.1% better than a comparable multiclass logistic regression model.Conclusions: Elderly UIs have poorer health status compared with permanent residents and citizens in the United States. Our newly developed machine learning model demonstrates a powerful support tool for designing health intervention programs that target UIs in the United States.

Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data

In the field of remote sensing, most of the feature indexes are obtained based on expert knowledge or domain analysis. With the rapid development of machine learning and artificial intelligence, this method is time-consuming and lacks flexibility, and the indexes obtained cannot be applied to all areas. In order to not rely on expert knowledge and find the effective feature index with regard to a certain material automatically, this paper proposes a data-driven method to learn interactive features for hyperspectral remotely sensed data based on a sparse multiclass logistic regression model. The key point explicitly expresses the interaction relationship between original features as new features by multiplication or division operation in the logistic regression. Through the strong constraint of the L1 norm, the learned features are sparse. The coefficient value of the corresponding features after sparse represents the basis for judging the importance of the features, and the optimal interactive features among the original features. This expression is inspired by the phenomenon that usually the famous indexes we used in remote sensing, like NDVI, NDWI, are the ratio between different spectral bands, and also in statistical regression, the relationship between features is captured by feature value multiplication. Experiments were conducted on three hyperspectral data sets of Pavia Center, Washington DC Mall, and Pavia University. The results for binary classification show that the method can extract the NDVI and NDWI autonomously, and a new type of metal index is proposed in the Pavia University data set. This framework is more flexible and creative than the traditional method based on laboratory research to obtain the key feature and feature interaction index for hyperspectral remotely sensed data.

Machine learning for decoding linear block codes: case of multi-class logistic regression model

<p>Facing the challenge of enormous data sets variety, several machine learning-based algorithms for prediction (e.g, Support vector machine, multi layer perceptron and logistic regression) have been highly proposed and used over the last years in many fields. Error correcting codes (ECCs) are extensively used in practice to protect data against damaged data storage systems and against random errors due to noise effects. In this paper, we will use machine learning methods, especially multi-class logistic regression combined with the famous syndrome decoding algorithm. The main idea behind our decoding method which we call logistic regression decoder (LRDec) is to use the efficient multi-class logistic regression models to find errors from syndromes in linear codes such as bose, ray-chaudhuri and hocquenghem (BCH), and the quadratic residue (QR). Obtained results of the proposed decoder have a significant benefit in terms of bit error rate (BER) for random binary codes. The comparison of our decoder with many competitors proves its power. The proposed decoder has reached a success percentage of 100% for correctable errors in the studied codes.</p>

Farmers' disaster preparedness and quality of life in earthquake-prone areas: The mediating role of risk perception

Individuals' disaster preparedness may indirectly affect their quality of life through the way they perceive risk. Based on the revised Protective Action Decision Model, using a survey of 327 households in the hardest-hit areas of the Wenchuan and Lushan earthquakes, this study has used the ordinal multi-class logistic regression model to analyze the correlation between farmers' disaster preparedness and their quality of life, and, further, to test the mediating effect of disaster risk perception between disaster preparedness and quality of life. The results show that: (1) disaster-preparedness behavior has a significant positive effect on individuals' happiness, life satisfaction, and general health; (2) compared with farmers who have not prepared for disasters, farmers who have prepared will perceive a lower disaster risk; (3) as for the general health of individuals, the perception of the possibility of disaster is an intermediary factor for the impact of disaster preparedness on quality of life. Policymakers should consider improving people's quality of life by changing their disaster-preparedness behaviors, thus improving their well-being.

Classification of the Disposition of Patients Hospitalized with COVID-19: Reading Discharge Summaries Using Natural Language Processing.

BackgroundMedical notes are a rich source of patient data; however, the nature of unstructured text has largely precluded the use of these data for large retrospective analyses. Transforming clinical text into structured data can enable large-scale research studies with electronic health records (EHR) data. Natural language processing (NLP) can be used for text information retrieval, reducing the need for labor-intensive chart review. Here we present an application of NLP to large-scale analysis of medical records at 2 large hospitals for patients hospitalized with COVID-19.ObjectiveOur study goal was to develop an NLP pipeline to classify the discharge disposition (home, inpatient rehabilitation, skilled nursing inpatient facility [SNIF], and death) of patients hospitalized with COVID-19 based on hospital discharge summary notes.MethodsText mining and feature engineering were applied to unstructured text from hospital discharge summaries. The study included patients with COVID-19 discharged from 2 hospitals in the Boston, Massachusetts area (Massachusetts General Hospital and Brigham and Women’s Hospital) between March 10, 2020, and June 30, 2020. The data were divided into a training set (70%) and hold-out test set (30%). Discharge summaries were represented as bags-of-words consisting of single words (unigrams), bigrams, and trigrams. The number of features was reduced during training by excluding n-grams that occurred in fewer than 10% of discharge summaries, and further reduced using least absolute shrinkage and selection operator (LASSO) regularization while training a multiclass logistic regression model. Model performance was evaluated using the hold-out test set.ResultsThe study cohort included 1737 adult patients (median age 61 [SD 18] years; 55% men; 45% White and 16% Black; 14% nonsurvivors and 61% discharged home). The model selected 179 from a vocabulary of 1056 engineered features, consisting of combinations of unigrams, bigrams, and trigrams. The top features contributing most to the classification by the model (for each outcome) were the following: “appointments specialty,” “home health,” and “home care” (home); “intubate” and “ARDS” (inpatient rehabilitation); “service” (SNIF); “brief assessment” and “covid” (death). The model achieved a micro-average area under the receiver operating characteristic curve value of 0.98 (95% CI 0.97-0.98) and average precision of 0.81 (95% CI 0.75-0.84) in the testing set for prediction of discharge disposition.ConclusionsA supervised learning–based NLP approach is able to classify the discharge disposition of patients hospitalized with COVID-19. This approach has the potential to accelerate and increase the scale of research on patients’ discharge disposition that is possible with EHR data.

The Influence of Human Capital and Social Capital on the Gendered Division of Labor in Peasant Family in Sichuan, China

Based on the disordered multi-class logistic regression model, using the data from the rural household survey conducted by the Sichuan Rural Development Survey Team in 2016, we analyzed the impact of human capital and social capital on the rural division of labor and gender division of labor in China. In general, human capital factors and social capital factors have important influences on rural family division decision-making and gender division of labor: the improvement of education level promotes the choice of non-agricultural work for both husband and wife. The overall family labor force has a negative impact on the choice of the wife to engage in non-agricultural work alone. Compared with men, women’s family division is affected by more kinds of social capital. The conclusion provides an important policy idea for promoting the rural labor force to achieve stable employment and urbanization strategies in the non-agricultural sector and to promote rural revitalization.

Transfer learning efficiently maps bone marrow cell types from mouse to human using single-cell RNA sequencing

Biomedical research often involves conducting experiments on model organisms in the anticipation that the biology learnt will transfer to humans. Previous comparative studies of mouse and human tissues were limited by the use of bulk-cell material. Here we show that transfer learning—the branch of machine learning that concerns passing information from one domain to another—can be used to efficiently map bone marrow biology between species, using data obtained from single-cell RNA sequencing. We first trained a multiclass logistic regression model to recognize different cell types in mouse bone marrow achieving equivalent performance to more complex artificial neural networks. Furthermore, it was able to identify individual human bone marrow cells with 83% overall accuracy. However, some human cell types were not easily identified, indicating important differences in biology. When re-training the mouse classifier using data from human, less than 10 human cells of a given type were needed to accurately learn its representation. In some cases, human cell identities could be inferred directly from the mouse classifier via zero-shot learning. These results show how simple machine learning models can be used to reconstruct complex biology from limited data, with broad implications for biomedical research.

Diagnostic value and clinical significance of magnetic resonance imaging with the FS-PD-TSE sequence in diagnosing lumbar cartilaginous endplate failure.

The aims were to use magnetic resonance imaging (MRI) to compare the efficacy of fat-suppressed proton-density turbo-spin-echo (FS-PD-TSE) images and T1-weighted (T1WIs) and T2-weighted images (T2WIs) in identifying cartilaginous endplate failure (CEF), and to propose a modified Rajasekaran classification based on the FS-PD-TSE sequence. Thirty-one lumbar disc herniation (LDH) cases were enrolled. Totally, 155 discs and 310 endplates were evaluated by MRI with T1W, T2W, and FS-PD-TSE sequences. Disc degeneration (DD), LDH grades, and the total endplate score (TEPS) of CEF were evaluated. Chi-square, Spearman rank correlation analysis, and multiclass logistic regression were used to compared the sensitivity in diagnosing CEF. A modified Rajasekaran classification based on FS-PD-TSE sequence was established to diagnose CEF. The multiclass logistic regression model was used to analyse the relationship between modified Rajasekaran classification and DD and LDH. There were 117 (75.5%) segments with CEF in T1WIs, 115 (74.2%) in T2WIs, and 127 (81.9%) in FS-PD-TSE, respectively. Chi-square test showed FS-PD-TSE images were more sensitive than T1WIs and T2WIs (P < 0.05). Spearman rank correlation analysis revealed a significant correlation between TEPS and LDH and DD in T1WIs, T2WIs, and FS-PD-TSE images (P < 0.05). A multiclass logistic regression model showed that the incidence of DD and LDH significantly increased accordingly with increases in modified Rajasekaran classification (P < 0.05). The FS-PD-TSE sequence has high diagnostic value for lumbar CEF. CEF is a risk factor for LDH. The new classification for lumbar CEF based on the FS-PD-TSE sequence has good predictive ability for LDH and DD. These slides can be retrieved under Electronic Supplementary Material.

Dependence of farmers' livelihoods on environmental resource in key ecological function area: A case study of Gannan Plateau, China

The key ecological function area plays an important role in providing ecosystem service. As the main players of economic activities in this area, the excessive dependence of local farmers on environmental resource will seriously influence ecological environment quality, and thus impair the ecosystem service. At present, it is urgent to explore the dependence of farmers' livelihoods on environmental resource and its influencing factors in key ecological function areas, which would provide reference for the formulation of environmental friendly regional development policies. We selected the Gannan Plateau as study area. Based on 581 survey data, we analyzed the dependence of farmers' livelihood on environmental resource, and used the ordinal multi-class Logistic regression model to identify its key impact factors. The results showed that local farmers were highly dependent on environmental resource in Gannan Plateau, which was mainly reflected in three aspects of food self-sufficiency, daily energy consumption and income source, with the dependence of 57.3%, 56.9% and 37.4%, respectively. There were significant differences in the dependence of different types of farmers' livelihoods on environmental resource, in that farmers with higher levels of education and higher non-agricultural levels had lower dependence and the farmers with high dependency ratio were more dependent on environmental resources. The factors including family dependency ratio, labor education level, proportion of migrant workers in labor force, family member leadership, ecological policy, and altitude had significant impact on farmers' environmental resource dependence. Among them, family dependency ratio and altitude had positive impact on it, while the proportion of migrant workers in labor force, labor education level, family member leadership, ecological policy had negative impact.

RAFFIA: Short-term Forest Fire Danger Rating Prediction via Multiclass Logistic Regression

Forest fire prevention is important because of human communities near forests or in the wildland-urban interfaces. Short-term forest fire danger rating prediction is an effective way to provide early guidance for forest fire managers. It can therefore effectively protect the forest resources and enhance the sustainability of the forest ecosystem. However, relevant existing forest fire danger rating prediction models operate well only when applied to distinct climates and fuel types separately. There are desires for an effective methodology, which can construct a specific short-term prediction model according to an evaluation of the data from that specific region. Moreover, a suitable method for prediction model construction needs to deal with some big data related computing challenges (i.e., data diversity coupled with complexity of solution space, and the requirement of real-time forest fire prevention application) when massively observed heterogeneous parameters are available for prediction (e.g., meteorology factor, the amount of litter in the area, soil moisture, etc.). To capture the influences of multiple prediction factors on the prediction results and effectively learn from fast cumulative historical big data, artificial intelligence methods are investigated in this paper, yielding a short-term Ratings of Forest Fire Danger Prediction via Multiclass Logistic Regression (or RAFFIA) model for forest fire danger rating online prediction. Experimental evaluations conducted on a sensor-based forest fire prevention experimental station show that RAFFIA (with 98.71% precision and 0.081 root mean square error) is more effective than the Least Square Fitting Regression (LSFR) and Random Forests (RF) prediction models.

Automatic assessment of voice quality in the context of multiple annotations.

Approaches to evaluate voice quality include perceptual analysis, and acoustical analysis. Perceptual analysis is subjective and depends mostly on the ability of a specialist to assess a pathology, whereas acoustical analysis is objective, but highly relies on the quality of the so called annotations that the specialist assigns to the voice signal. The quality of the annotations for acoustical analysis depends heavily on the expertise and knowledge of the specialist. We face a scenario where we have annotations performed by several specialists with different levels of expertise and knowledge. Traditional pattern recognition methods employed in acoustical analysis are no longer applicable, since these methods are designed for scenarios where a "ground-truth" label is assigned by the specialist. In this paper, we apply recent developments in machine learning for taking into account multiple annotators for acoustical analysis of voice signals. For the classification step we compare two techniques, one of them based on Gaussian Processes for regression with multiple annotators, and the other is a multi-class Logistic Regression model that measures the annotator performance in terms of sensitivity and specificity. The performance of classifiers is assessed in terms of Cohen's Kappa index. Results show that the multi-annotator classification schemes have better performance when compared to techniques based on a traditional classifier where the true label is estimated from the multiple annotations available using majority voting.

Purpura Haemorrhagica

<h3>ABSTRACT</h3> <h3>Object</h3> It remains unelucidated regarding the effects of vaccination on disease severity and factors for viral clearance and hospitalization in omicron-infected patients. <h3>Methods</h3> The clinical manifestations of 3,265 Omicron-infected patients (BA.2 variant; the Omicron group) were compared with those of 226 Delta-infected patients (the Delta group).A Multi-class logistic regression model was employed to analyze the impacts of vaccination doses and intervals on disease severity; a logistic regression model to evaluate the risk factors for hospitalization; R 4.1.2 data analysis to investigate the factors for time for nucleic acid negativization (NAN). <h3>Results</h3> Compared with the Delta group, the Omicron group reported a fast transmission, mild symptoms, and lower severity incidence, and a significant inverse correlation of vaccination dose with clinical severity (OR: 0.803, 95%CI: 0.742-0.868, p&lt;0.001). Of the 7 or 5 categories of vaccination status, the risk of severity significantly decreased only at ≥21 days after three doses (OR: 0.618, 95% CI: 0.475-0.803, p&lt;0.001; OR: 0.627, 95% CI: 0.482-0.815, p&lt;0.001, respectively). The Omicron group also reported underlying illness as an independent factor for hospitalization, sore throat as a protective factor, and much shorter time for NAN [15 (12,19) vs. 16 (12,22), p&lt;0.05]. NAN was associated positively with age, female gender, fever, cough, and disease severity, but negatively with vaccination doses. <h3>Conclusion</h3> Booster vaccination should be advocated for COVID-19 pandemic-related control and prevention policies and adequate precautions should be taken for patients with underlying conditions.