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Professional sport facility sustainability initiatives offer sport organizations an opportunity to demonstrate congruence with societal concern for the environment, an effort that also affects stadia revenue generation. Guided by diffusion of innovations theory, this study harnessed diffusion modeling and logistic regression to determine how quickly renewable energy source adoption is diffusing across 175 professional sport stadia in the United States and Canada and the factors catalyzing early renewable energy source adoption. Results revealed 86 (49%) facilities adopted at least one type of renewable energy source, with solar emerging as the predominant technology adopted (68 total adoptions). Full diffusion for renewable source adoption was predicted for 2061 (p =.0094, q = 0.1404, root mean square error = 3.25, mean absolute error = 2.51), while not all renewable energy sources were predicted to fully diffuse (wind; p =.0117, q = −0.0710, root mean square error =.853, mean absolute error = 0.675). New stadia construction during the time of adoption, facility type, and geographical social systems emerged as significant factors catalyzing adoption in the early majority. [ABSTRACT FROM AUTHOR]

OBJECTIVE: This study evaluates regularization variants in logistic regression (L1, L2, ElasticNet, Adaptive L1, Adaptive ElasticNet, Broken adaptive ridge [BAR], and Iterative hard thresholding [IHT]) for discrimination and calibration performance, focusing on both internal and external validation. MATERIALS AND METHODS: We use data from 5 US claims and electronic health record databases and develop models for various outcomes in a major depressive disorder patient population. We externally validate all models in the other databases. We use a train-test split of 75%/25% and evaluate performance with discrimination and calibration. Statistical analysis for difference in performance uses Friedmans test and critical difference diagrams. RESULTS: Of the 840 models we develop, L1 and ElasticNet emerge as superior in both internal and external discrimination, with a notable AUC difference. BAR and IHT show the best internal calibration, without a clear external calibration leader. ElasticNet typically has larger model sizes than L1. Methods like IHT and BAR, while slightly less discriminative, significantly reduce model complexity. CONCLUSION: L1 and ElasticNet offer the best discriminative performance in logistic regression for healthcare predictions, maintaining robustness across validations. For simpler, more interpretable models, L0-based methods (IHT and BAR) are advantageous, providing greater parsimony and calibration with fewer features. This study aids in selecting suitable regularization techniques for healthcare prediction models, balancing performance, complexity, and interpretability.

The disparity in absenteeism between kindergarteners with and without disabilities is a persistent phenomenon across schools in the United States and reflects ongoing systemic inequities that disadvantage young children with disabilities. Yet, evidence of factors underlying this disparity remains less well understood, limiting the ability for schools to transform how they support students with disabilities in ways that could help narrow the disparity. In our study, we investigate how a set of factors, grounded in a socioecological framework of absenteeism, correlates with chronic absenteeism in kindergarteners with and without disabilities. Importantly, we examine the extent to which distributional differences in these factors between children with and without disabilities explain the attendance disparity. Our study draws upon a nationally representative sample of N = 13,860 kindergarteners (52 % male, 48 % female, M age = 5.5 years, n = 1240 with disabilities) from the Early Childhood Longitudinal Survey Kindergarten Class of 2010–11. We analyze our data using multiple logistic regression to identify significant factors that correlate with chronic absenteeism and leverage the Kitagawa-Blinder-Oaxaca decomposition method to describe how distributional differences in those factors contribute to absenteeism disparities. Results show that internalizing behaviors in students with disabilities and their parents’ health are significantly associated with chronic absenteeism as well as help explain the disparity (12 % and 3 % of the disparity, respectively). Importantly, since differences in internalizing behaviors can be generated, in part, by school practices and policies that treat early elementary students with disabilities inequitably, our work suggests that reducing gaps in early absenteeism may require schools to reduce exposure to experiences that can lead to internalizing problem behaviors in young children with disabilities.

This study investigated whether machine learning (ML) has better predictive accuracy than logistic regression analysis (LR) for gait independence at discharge in subacute stroke patients (n = 843) who could not walk independently at admission. We developed prediction models using LR and five ML algorithms—specifically, the decision tree (DT), support vector machine, artificial neural network, ensemble learning, and k-nearest neighbor methods. Functional Independence Measure sub-items were used to evaluate the ability to walk independently. Model predictive accuracies were evaluated using areas under receiver operating characteristic curves (AUCs) as well as accuracy, precision, recall, F1 score, and specificity. The AUC for DT (0.812) was significantly lower than those for the other algorithms (p < 0.01); however, the AUC for LR (0.895) did not differ significantly from those for the other models (0.893–0.903). Other performance metrics showed no substantial differences between LR and ML algorithms. In conclusion, the DT algorithm had significantly low predictive accuracy, and LR showed no significant difference in predictive accuracy compared with the other ML algorithms. As its predictive accuracy is similar to that of ML, LR can continue to be used for predicting the prognosis of gait independence, with additional advantages of being easily understandable and manually computable. [ABSTRACT FROM AUTHOR]

Background/objectives: The race-sex differences in emergency department (ED) disposition decisions have been reported widely. Our objective is to identify demographic and clinical subgroups for which this difference is most pronounced, which will facilitate future targeted research on potential disparities and interventions. Methods: We performed a retrospective analysis of 93 987 White and African-American adults assigned an Emergency Severity Index of 3 at 3 large EDs from January 2019 to February 2020. Using random forests, we identified the Elixhauser comorbidity score, age, and insurance status as important variables to divide data into subpopulations. Logistic regression models were then fitted to test race-sex differences within each subpopulation while controlling for other patient characteristics and ED conditions. Results: In each subpopulation, African-American women were less likely to be admitted than White men with odds ratios as low as 0.304 (95% confidence interval (CI): [0.229, 0.404]). African-American men had smaller admission odds compared to White men in subpopulations of 41+ years of age or with very low/high Elixhauser scores, odds ratios being as low as 0.652 (CI: [0.590, 0.747]). White women were less likely to be admitted than White men in subpopulations of 18 to 40 or 41 to 64 years of age, with low Elixhauser scores, or with Self-Pay or Medicaid insurance status with odds ratios as low as 0.574 (CI: [0.421, 0.784]). Conclusions: While differences in likelihood of admission were lessened by younger age for African-American men, and by older age, higher Elixhauser score, and Medicare or Commercial insurance for White women, they persisted in all subgroups for African-American women. In general, patients of age 64 years or younger, with low comorbidity scores, or with Medicaid or no insurance appeared most prone to potential disparities in admissions. [ABSTRACT FROM AUTHOR]

Introduction : This study presents a comprehensive analysis of wrong-way driving (WWD) fatal crashes on divided highways in the United States over a 17-year period, from 2004 to 2020. The study aims to uncover trends, distribution patterns, and factors contributing to these fatal crashes. Data were extracted from the National Highway Traffic Safety Administration (NHTSA) Fatality Analysis Reporting System (FARS) database. Methods : Descriptive statistical analysis was used to reveal general crash characteristics, while trends were updated through an examination of the annual occurrence of WWD fatal crashes. The study further employed binomial logistic regression to compute odds ratios, identifying significant contributing factors. These factors encompassed temporal variables, crash characteristics, and driver characteristics. The odds ratios shed light on the relationship between WWD fatal crashes and other fatal crashes, allowing for the identification of key elements that drive WWD incidents. Results : On average, 302 WWD fatal crashes occurred annually, resulting in 6,953 fatalities during the study period. The frequency of WWD fatal crashes remained relatively stable, with a slight increase over time. According to the model, variables include day of week, time of day, month, lighting conditions, weather conditions, roadway profile, collision type, passenger presence, driver age, gender, license status, and driver injury severity were found to significantly impact the occurrence of WWD fatal crashes. One significant finding is that road profiles like sag curves or hillcrests can increase the likelihood of WWD fatal crashes. Practical Application : The findings of this study contribute to an improved understanding of WWD fatal crashes on divided highways, thereby aiding in the development of strategies for prevention and mitigation. [ABSTRACT FROM AUTHOR]

Accurate diagnosis and effective treatment of thyroid conditions, such as hypothyroidism and hyperthyroidism, are crucial due to their wide-ranging symptoms and consequences. However, conventional back-propagation neural networks have limitations, including slow convergence and susceptibility to local minima. To deal with these problems, the paper suggests an integrated strategy that includes back-propagation (BP) and genetic algorithms (GA). The suggested method uses evolutionary algorithms to investigate various weight combinations and back-propagation to modify weights in response to the discrepancy between expected and actual results. The study evaluated the method using three steps of experimentation, including network building, local search and optimization, and evaluation, on a real-world dataset of thyroid illnesses. The obtained results are very satisfying and promising, indicating that the MLP-GA/BP model is robust enough to detect and categorize thyroid disorders effectively and efficiently. This makes it a reliable diagnostic tool for medical practitioners, allowing them to effectively diagnose and treat patients with thyroid diseases. [ABSTRACT FROM AUTHOR]

Chile had a violent military coup (1973–1990) that resulted in 3,000 victims declared detained, missing or killed; many are still missing and unidentified. Currently, the Human Rights Unit of the Forensic Medical Service in Chile applies globally recognised forensic anthropological approaches, but many of these methods have not been validated in a Chilean sample. As current research has demonstrated population-specificity with extant methods, the present study aims to validate sex estimation methods in a Chilean population and thereafter establish population-specific equations. A sample of 265 os coxae of known age and sex of adult Chileans from the Santiago Subactual Osteology Collection were analysed. Visual assessment and scoring of the pelvic traits were performed in accordance with the Phenice (1969) and Klales et al. (2012) methods. The accuracy of Phenice (1969) in the Chilean sample was 96.98%, with a sex bias of 7.68%. Klales et al. (2012) achieved 87.17% accuracy with a sex bias of -15.39%. Although both methods showed acceptable classification accuracy, the associated sex bias values are unacceptable in forensic practice. Therefore, six univariate and eight multivariate predictive models were formulated for the Chilean population. The most accurate univariate model was the ventral arc at 96.6%, with a sex bias of 5.2%. Classification accuracy using all traits was 97.0%, with a sex bias of 7.7%. This study provides Chilean practitioners a population-specific morphoscopic standard with associated classification probabilities acceptable to accomplish legal admissibility requirements in human rights and criminal cases specific to the second half of the 20th century. [ABSTRACT FROM AUTHOR]

In this study we tested classification performance of a sex estimation method from the mandible originally developed by Sella-Tunis et al. (2017) on a heterogeneous Israeli population. Mandibular linear dimensions were measured on 60 CT scans derived from the Czech living population. Classification performance of Israeli discriminant functions (DFs-IL) was analyzed in comparison with calculated Czech discriminant functions (DFs-CZ) while different posterior probability thresholds (currently discussed in the forensic literature) were employed. Our results comprehensively illustrate sensitivity of different discriminant functions to population differences in body size and degree of sexual dimorphism. We demonstrate that the error rate may be biased when presented per posterior probability threshold. DF-IL 1 showed least sensitivity to population origin and fulfilled criteria of sufficient classification performance when applied on the Czech sample with a minimum posterior probability threshold of 0.88 reaching overall accuracy ≥ 95%, zero sex bias, and 80% of classified individuals. The last parameter was higher in DF-CZ 1 which was the main difference between those two DFs suggesting relatively low dependance on population origin. As the use of population-specific methods is often prevented by complicated assessment of population origin, DF-IL 1 is a candidate for a sufficiently robust method that could be reliably applied outside the reference sample, and thus, its classification performance deserves further testing on more population samples. [ABSTRACT FROM AUTHOR]

The association between the Triglyceride-glucose (TyG) index and depression has been observed, yet its confirmation within peri- and postmenopausal demographics remains elusive. Consequently, the principal aim of this investigation is to explore the nexus between TyG-related indicators and depressive symptoms among pre- and postmenopausal women. The data utilized in this study were obtained from the National Health and Nutrition Examination Survey (NHANES) conducted from 2013 to 2016. The patients were divided into three groups based on TyG, Triglyceride-Glucose-Body Mass Index (TyG-BMI), Triglyceride-Glucose-Waist Circumference (TyG-WC), and Triglyceride-Glucose-Waist-to-Height Ratio (TyG-WHtR): Q1 (1st quintile), Q2 (2nd quintile), and Q3 (3rd quintile). Further exploration of the differences between these groups was conducted. Employing logistic regression, stratified analysis, restricted cubic splines, and subgroup analyses, we scrutinized the correlation between TyG-related indicators and depressive symptoms in both premenopausal and postmenopausal women. Furthermore, sensitivity analyses were conducted to assess the durability and uniformity of this relationship. In premenopausal women, there was a consistent independent positive correlation between TyG-BMI, TyG-WC, and TyG-WHtR with depressive symptoms across all three models, while TyG itself did not show a significant association. In Models 1 and 2, TyG-BMI exhibited a higher odds ratio (OR) value than the other two indicators [Model 1, Q3 OR (95 % confidence interval, CI) = 3.37 (1.91–5.94); Model 2, Q3 OR (95 % CI) = 3.03 (1.67–5.52)]. In Models 3, TyG-WHtR demonstrates a more significant association with depressive symptoms [Model 3, Q3 OR (95 % CI) = 2.85 (1.55–5.27)]. This correlation does not manifest in menopausal women. In premenopausal women, TyG-BMI, TyG-WC, and TyG-WHtR exhibited a positive and linear relationship with depressive symptoms. Furthermore, the analysis revealed that the combined measures of TyG-BMI, TyG-WC, and TyG-WHtR offered greater precision and sensitivity in assessing this association compared to TyG alone. • A cross-sectional analysis first examined the link between TyG indicators and depressive symptoms in pre- and postmenopausal women. • A strong correlation was found between TyG-BMI, TyG-WC, and TyG-WHtR, and depression in premenopausal women. • Using multiple indicators was more effective than the TyG index alone in predicting depression risk. [ABSTRACT FROM AUTHOR]

A novel nomogram incorporating artificial intelligence (AI) and clinical features for enhanced ultrasound prediction of benign and malignant breast masses. This study analyzed 340 breast masses identified through ultrasound in 308 patients. The masses were divided into training (n = 260) and validation (n = 80) groups. The AI-based analysis employed the Samsung Ultrasound AI system (S-detect). Univariate and multivariate analyses were conducted to construct nomograms using logistic regression. The AI-Nomogram was based solely on AI results, while the ClinAI- Nomogram incorporated additional clinical factors. Both nomograms underwent internal validation with 1000 bootstrap resamples and external validation using the independent validation group. Performance was evaluated by analyzing the area under the receiver operating characteristic (ROC) curve (AUC) and calibration curves. The ClinAI-Nomogram, which incorporates patient age, AI-based mass size, and AI-based diagnosis, outperformed an existing AI-Nomogram in differentiating benign from malignant breast masses. The ClinAI-Nomogram surpassed the AI-Nomogram in predicting malignancy with significantly higher AUC scores in both training (0.873, 95% CI: 0.830–0.917 vs. 0.792, 95% CI: 0.748–0.836; p = 0.016) and validation phases (0.847, 95% CI: 0.763–0.932 vs. 0.770, 95% CI: 0.709–0.833; p < 0.001). Calibration curves further revealed excellent agreement between the ClinAI-Nomogram's predicted probabilities and actual observed risks of malignancy. The ClinAI- Nomogram, combining AI alongside clinical data, significantly enhanced the differentiation of benign and malignant breast masses in clinical AI-facilitated ultrasound examinations. [ABSTRACT FROM AUTHOR]

Nowadays, electroencephalogram (EEG) signals are the major part of recognizing emotions as the signals contain more brain-related information. Several traditional recognition methodologies have been developed, but still, exist with certain challenges that degrade and mislead the recognition process. Some models are facing issues based on insignificant features, poor generalization capability, and computational burden. To alleviate such issues, an effective emotion recognition model is proposed using a heuristic-aided transformer-based approach. Initially, the input EEG signals are gathered and decomposed via the 5-level Discrete Wavelet Transform (DWT). Then, the decomposed signals are fed to the feature extraction process, where the feature extraction is carried out by using Logistic Regression (LR) approach. From the decomposed signals, the Logistic Regression (LR) is used for getting the noteworthy features and then, the same decomposed signals are fed to the optimal weighted feature selection, which is carried out using Adaptive Bald Eagle Search Optimization (ABESO) algorithm. Subsequently, the hybrid weighted feature selection approach is introduced for getting the weighted features, where the weight is optimized with the adoption of the ABESO algorithm. At last, the selected hybrid weighted features are sent to the recognition phase, where the Optimized Block Recurrent Transformer (OBRT), where performance gets enhanced by tuning the parameter with the developed ABESO algorithm. The accuracy and precision rate of the designed approach are attained as 96% and 94%. The performance of the model is assessed and the findings have shown that better performance that makes the system more feasible to recognize human emotions. [ABSTRACT FROM AUTHOR]

Objectives: Noncarious cervical lesions (NCCLs) are multifactorial and can be caused by the anatomical structure of the teeth, erosion, abrasion and abnormal occlusion. The aim of this case–control study was to explore the risk factors for NCCLs. Methods: Cone‐beam computed tomography was used to determine whether a wedge‐shaped defect existed at the cementoenamel junction. We compared 63 participants with NCCLs with 63 controls without NCCLs, matched for sex, age (±1 year) and toothbrushing‐related factors (e.g., type of bristle and brushing patterns, frequency and strength). All participants were asked to complete a questionnaire about self‐administered daily diet habits and health condition. Univariate and multivariate logistic regression analyses were conducted to determine the risk factors for NCCLs. Results: Significant variables in the univariate analysis (i.e., p <.2) included frequency of carbonated beverage consumption, sella‐nasion‐point B angle (SNB) and Frankfort‐mandibular plane angle (FMA). Multivariate logistic regression demonstrated that the consumption frequency of carbonated beverages (odds ratio [OR] = 3.147; 95% confidence interval [CI], 1.039–9.532), FMA (OR = 1.100; 95% CI, 1.004–1.204) and SNB (OR = 0.896; 95% CI, 0.813–0.988) was independent influencing factors. The area under the receiver operating characteristic curve (AUC) value of regression Model 1 (established with the frequency of carbonated beverage consumption, FMA, SNB and sleep bruxism) was 0.700 (95% CI, 0.607–0.792; p <.001), and that of regression Model 2 (established using the frequency of carbonated beverage consumption, FMA and SNB) was 0.704 (95% CI, 0.612–0.796; p <.001). Conclusions: The consumption frequency of carbonated beverages and FMA was risk factors for NCCLs; the higher the frequency of carbonated beverage consumption and FMA, the higher was the probability of NCCLs. SNB was a protective factor for NCCL occurrence; the larger the SNB, the lower was the probability of NCCL occurrence. These findings have further clarified the aetiology of NCCLs and provided clinicians with valuable insights into strategies for preventing the loss of dental tissue. [ABSTRACT FROM AUTHOR]

With mixed-effects regression models becoming a mainstream tool for every psycholinguist, there has become an increasing need to understand them more fully. In the last decade, most work on mixed-effects models in psycholinguistics has focused on properly specifying the random-effects structure to minimize error in evaluating the statistical significance of fixed-effects predictors. The present study examines a potential misspecification of random effects that has not been discussed in psycholinguistics: violation of the single-subject-population assumption, in the context of logistic regression. Estimated random-effects distributions in real studies often appear to be bi- or multimodal. However, there is no established way to estimate whether a random-effects distribution corresponds to more than one underlying population, especially in the more common case of a multivariate distribution of random effects. We show that violations of the single-subject-population assumption can usually be detected by assessing the (multivariate) normality of the inferred random-effects structure, unless the data show quasi-separability, i.e., many subjects or items show near-categorical behavior. In the absence of quasi-separability, several clustering methods are successful in determining which group each participant belongs to. The BIC difference between a two-cluster and a one-cluster solution can be used to determine that subjects (or items) do not come from a single population. This then allows the researcher to define and justify a new post hoc variable specifying the groups to which participants or items belong, which can be incorporated into regression analysis. [ABSTRACT FROM AUTHOR]

This study designs a double-bounded dichotomous questionnaire, and uses the Contingent Valuation Method (CVM) to estimate residents' willingness to pay(WTP) for water pollution control along the Taihu Lake Basin. The results of the returned questionnaire show that 82.76% of the residents are willing to pay. CVM estimation results show that the average WTP of residents for water pollution control is 138.86 yuan/year. In addition, the influencing factors of WTP are explored using a Logistic regression model, and the heterogeneity of WTP among residents of different genders is analyzed. The study found that: (1) The younger the residents, the higher their WTP; (2) The higher the income, the higher the residents' WTP for water pollution control; (3) Residents with higher educational level are more willing to pay; (4) The higher the degree of residents' understanding of water pollution control policies, the higher the WTP; (5) The higher the degree of residents' recognition of pro-environmental behavior, the higher the WTP; (6) Male residents' WTP is mainly affected by cognitive factors such as their understanding of governance policies and their approval of pro-environmental behaviors, while female residents' WTP is mainly affected by personal attributes, such as age, income, and the number of household laborers. Furthermore, this study proposes targeted measures to improve residents' WTP from three aspects: the government enriches the channels for residents to participate in water pollution control, the social media enriches the popularization of water environment knowledge, and the school strengthens the education of environmental protection knowledge, considering the differences in residents' characteristics. Therefore, this study can provide a theoretical reference and decision-making basis for encouraging residents to participate in water pollution control, promote the construction of a beautiful watershed, and provide a reference for other basins. [ABSTRACT FROM AUTHOR]

A biomarker is a measured indicator of a variety of processes, and is often used as a clinical tool for the diagnosis of diseases. While the developmental process of biomarkers from lab to clinic is complex, initial exploratory stages often focus on characterizing the potential of biomarkers through utilizing various statistical methods that can be used to assess their discriminatory performance, establish an appropriate cut-off that transforms continuous data to apt binary responses of confirming or excluding a diagnosis, or establish a robust association when tested against confounders. This review aims to provide a gentle introduction to the most common tools found in diagnostic biomarker studies used to assess the performance of biomarkers with an emphasis on logistic regression. [ABSTRACT FROM AUTHOR]

Background Sri Lankan married women have been reported to experience higher rates of intimate partner violence (IPV). However, research on its impact on mental health and how socio-demographic factors contribute to this association is limited. Therefore, this study aimed to examine the impact of IPV on the mental health of married women in Sri Lanka. Methods In this study, data from 1611 married women who participated in the 2019 Sri Lankan Women's Wellbeing Survey were analyzed. Two binary outcomes were considered: married women's mental health and their suicidal ideation. Binary logistic regression models were used to assess the association between mental health and suicidal thoughts in relation to IPV while controlling for socio-demographic factors. Results The results revealed that married women who experienced any form of violence by their spouse had a higher risk of having poor mental health conditions [AOR = 2.88 (2.20, 3.78)] and suicidal thoughts [AOR = 5.84 (4.10, 8.32)] compared to those who did not experience IPV. Conclusions IPV is a substantial contributor to poor mental health and suicidal thoughts among Sri Lankan married women. There is an urgent need for policy interventions, such as community awareness programs, counseling services and enhanced legal protections for victims. [ABSTRACT FROM AUTHOR]

Background: Mild traumatic brain injury (mTBI) comprises a majority of traumatic brain injury (TBI) cases. While some mTBI would suffer neurological deterioration (ND) and therefore have poorer prognosis. This study was designed to develop the predictive model for the ND among mTBI using machine learning algorithms. Methods: mTBI patients recorded in the Medical Information Mart for Intensive Care-III were selected for the study. The ND was defined as a drop of Glasgow Coma Scale ≥ 2 within the first 7 day after admission. Eight machine learning algorithms were trained and validated with 5-fold cross validation including extreme gradient boosting, logistic regression, light gradient boosting machine, random forest, adaptive boosting, decision tree, complement naïve Bayes, and support vector machine. The value of eight machine learning algorithms was compared by the area under the receiver operating characteristic curve (AUC). Results: 361 mTBI patients suffered the ND with the incidence of 30.7%. The ND group had higher 30-day mortality (p = 0.001). In the training cohort of mTBI patients, the random forest performed the best on predicting the ND with the AUC of 1.000. The XGBoost and AdaBoost had an AUC of 0.827 and 0.815, respectively. The logistic regression performed the best on predicting the ND in the validation cohort with the AUC of 0.741. The XGBoost, random forest and AdaBoost had an AUC of 0.729, 0.735, 0.736 in the validation cohort, respectively. After adjusting confounding effects, the multivariate logistic regression found only two independent risk factors for the ND including Sequential Organ Failure Assessment (SOFA) (p < 0.001) and hypertension (p = 0.001). The logistic regression predictive model composed of SOFA and hypertension had an AUC of 0.741. Conclusions: SOFA score and complicated hypertension are two independent risk factors for the neurological deterioration among mTBI patients. The logistic regression predictive model incorporating SOFA and hypertension is helpful to identify mTBI patients with the high risk of ND. [ABSTRACT FROM AUTHOR]

A stroke is a dangerous, life-threatening disease that mostly affects people over 65, but an unhealthy diet is also contributing to the development of strokes at younger ages. Strokes can be treated successfully if they are identified early enough, and suitable therapies are available. The purpose of this study is to develop a stroke prediction model that will improve stroke prediction effectiveness as well as accuracy. Predicting whether someone is suffering from a stroke or not can be accomplished with this proposed machine learning algorithm. In this research, various machine learning techniques are evaluated for predicting stroke on the healthcare stroke dataset. The feature selection algorithms used here are gradient boosting and random forest, and classifiers include the decision tree classifier, Support Vector Machine (SVM) classifier, logistic regression classifier, gradient boosting classifier, random forest classifier, K neighbors classifier, and Xtreme gradient boosting classifier. In this process, different machine-learning approaches are employed to test predictive methods on different data samples. As a result obtained from the different methods applied, and the comparison of different classification models, the random forest model offers an accuracy rate of 98%. [ABSTRACT FROM AUTHOR]

Mild cognitive impairment (MCI) is a prodromal stage of Alzheimer's disease (AD) that causes a significant burden in caregiving and medical costs. Clinically, the diagnosis of MCI is determined by the impairment statuses of five cognitive domains. If one of these cognitive domains is impaired, the patient is diagnosed with MCI, and if two out of the five domains are impaired, the patient is diagnosed with AD. In medical records, most of the time, the diagnosis of MCI/AD is given, but not the statuses of the five domains. We may treat the domain statuses as missing variables. This diagnostic procedure relates MCI/AD status modeling to multiple‐instance learning, where each domain resembles an instance. However, traditional multiple‐instance learning assumes common predictors among instances, but in our case, each domain is associated with different predictors. In this article, we generalized the multiple‐instance logistic regression to accommodate the heterogeneity in predictors among different instances. The proposed model is dubbed heterogeneous‐instance logistic regression and is estimated via the expectation‐maximization algorithm because of the presence of the missing variables. We also derived two variants of the proposed model for the MCI and AD diagnoses. The proposed model is validated in terms of its estimation accuracy, latent status prediction, and robustness via extensive simulation studies. Finally, we analyzed the National Alzheimer's Coordinating Center‐Uniform Data Set using the proposed model and demonstrated its potential. [ABSTRACT FROM AUTHOR]

Background: Catheter-related thrombosis (CRT) is a common complication for patients who receive central venous catheter (CVC) placement. This study investigated the risk factors for CRT and developed a nomogram for CRT prediction among cancer patients. Methods: This nested case-control study was conducted in the Third Affiliated Hospital of Kunming Medical University between January 2019 and February 2021. Univariable and multivariable logistic regression analyses were used to identify the risk factors for CRT. A nomogram was developed to predict CRT. Receiver operating curves (ROC), calibration curves, and decision curves were used to evaluate the performance of the nomogram in the training and validation sets. Results: A total of 4,691 cancer patients were included in this study. Among them, 355 (7.57%) had CRT, and 70% of CRTs occurred in the first week of insertion. Among the 3,284 patients in the training set, the multivariable analysis showed that nine characteristics were independently associated with CRT, and a nomogram was constructed based on the multivariable analysis. The ROC analysis indicated good discrimination in the training set (area under the curve [AUC] = 0.832, 95% CI: 0.802–0.862) and the testing set (AUC = 0.827, 95% CI: 0.783–0.871) for the CRT nomogram. The calibration curves showed good calibration abilities, and the decision curves indicated the clinical usefulness of the prediction nomograms. Conclusion: The validated nomogram accurately predicts CRT occurrence in cancer patients. This model may assist clinicians in developing treatment plans for each patient. [ABSTRACT FROM AUTHOR]

Text classification plays a major role in research such as sentiment analysis, opinion mining, and customer feedback analysis. Text classification using hypergraph algorithms is effective in capturing the intricate relationships between words and phrases in documents. The method entails text preprocessing, keyword extraction, feature selection, text classification, and performance metric evaluation. Here, we proposed a Hypergraph Attention Layer with Logistic Regression (HGATT_LR) for text classification in the Amazon review data set. The essential keywords are extracted by utilizing the Latent Dirichlet Allocation (LDA) technique. To build a hypergraph attention layer, feature selection based on node-level and edge-level attention is assessed. The resultant features are passed as an input of Logistic regression for text classification. Through a comparison analysis of different text classifiers on the Amazon data set, the performance metrics are assessed. Text classification using hypergraph Attention Network has been shown to achieve 88% accuracy which is better compared to other state-of-the-art algorithms. The proposed model is scalable and may be easily enhanced with more training data. The solution highlights the utility of hypergraph approaches for text classification as well as their applicability to real-world datasets with complicated interactions between text parts. This type of analysis will empower the business people will improve the quality of the product. [ABSTRACT FROM AUTHOR]

Background: Patients with airway stenosis (AS) are associated with considerable morbidity and mortality after lung transplantation (LTx). This study aims to develop and validate machine learning (ML) models to predict AS requiring clinical intervention in patients after LTx. Methods: Patients who underwent LTx between January 2017 and December 2019 were reviewed. The conventional logistic regression (LR) model was fitted by the independent risk factors which were determined by multivariate LR. The optimal ML model was determined based on 7 feature selection methods and 8 ML algorithms. Model performance was assessed by the area under the curve (AUC) and brier score, which were internally validated by the bootstrap method. Results: A total of 381 LTx patients were included, and 40 (10.5%) patients developed AS. Multivariate analysis indicated that male, pulmonary arterial hypertension, and postoperative 6-min walking test were significantly associated with AS (all P < 0.001). The conventional LR model showed performance with an AUC of 0.689 and brier score of 0.091. In total, 56 ML models were developed and the optimal ML model was the model fitted using a random forest algorithm with a determination coefficient feature selection method. The optimal model exhibited the highest AUC and brier score values of 0.760 (95% confidence interval [CI], 0.666–0.864) and 0.085 (95% CI, 0.058–0.117) among all ML models, which was superior to the conventional LR model. Conclusions: The optimal ML model, which was developed by clinical characteristics, allows for the satisfactory prediction of AS in patients after LTx. [ABSTRACT FROM AUTHOR]

In the analysis of logistic regression models, various biased estimators have been proposed as an alternative to the maximum likelihood estimator (MLE) for estimating model parameters in the presence of multicollinearity. In this study, a new class of biased estimators called Logistic Ridge-type Estimator (LRTE) is proposed by generalizing the existing biased estimators that include two biasing parameters. The performance of the proposed estimator is compared with the other biased estimators in terms of the Matrix Mean Squared Error (MMSE). Two separate Monte Carlo simulation studies are conducted to investigate the performance of the proposed estimator. A numerical example is provided to demonstrate the performance of the proposed biased estimator. The results revealed that LRTE performed better than other existing biased estimators under the conditions investigated in this study. [ABSTRACT FROM AUTHOR]

Objective Methods Results Conclusion The diagnosis of symptomatic urinary stones during pregnancy is challenging; ultrasonography has a low specificity and sensitivity for diagnosing urinary stones. This study aimed to develop a clinical diagnostic model to assist clinicians in distinguishing symptomatic urinary stones in pregnant women.In this retrospective cohort study, we consecutively collected clinical data from pregnant women who presented with acute abdominal, lumbar, and lumbar and abdominal pain at the emergency department of our hospital between January 1, 2017, and December 31, 2019. To distinguish patients with urinary calculi from those without, we reviewed the follow‐up records within 2 weeks post‐consultation, ultrasonography results within 2 weeks, or self‐reports of stone passage within 2 weeks. We selected risk factors from the baseline clinical and laboratory data of patients to establish a diagnostic model.Of the total patients included in the study, 105 patients were diagnosed as having symptomatic urinary stones and 126 were determined to have abdominal pain for reasons other than urinary stones. The initial model had an area under the curve (AUC) of 0.9966. The No‐Lab Model had an AUC of 0.9856. The Lab Model had an AUC of 0.832. The Stone Model had an AUC of 0.9952. The simplified Stone Model did not show a decrease in discriminative ability.Of the four diagnostic models that we established for preliminary diagnosis of symptomatic urinary tract stones in pregnant women, the simplified Stone Model demonstrated excellent performance. Users can scan quick response codes to access web‐based diagnostic model interfaces, facilitating easy clinical operation. [ABSTRACT FROM AUTHOR]

Objectives: To develop a machine learning logistic regression algorithm that can classify patients with an idiopathic macular hole (IMH) into those with good or poor vison at 6 months after a vitrectomy. In addition, to determine its accuracy and the contribution of the preoperative OCT characteristics to the algorithm. Methods: This was a single-center, cohort study. The classifier was developed using preoperative clinical information and the optical coherence tomographic (OCT) findings of 43 eyes of 43 patients who had undergone a vitrectomy. The explanatory variables were selected using a filtering method based on statistical significance and variance inflation factor (VIF) values, and the objective variable was the best-corrected visual acuity (BCVA) at 6 months postoperation. The discrimination threshold of the BCVA was the 0.15 logarithm of the minimum angle of the resolution (logMAR) units. Results: The performance of the classifier was 0.92 for accuracy, 0.73 for recall, 0.60 for precision, 0.74 for F-score, and 0.84 for the area under the curve (AUC). In logistic regression, the standard regression coefficients were 0.28 for preoperative BCVA, 0.13 for outer nuclear layer defect length (ONL_DL), −0.21 for outer plexiform layer defect length (OPL_DL) − (ONL_DL), and −0.17 for (OPL_DL)/(ONL_DL). In the IMH form, a stenosis pattern with a narrowing from the OPL to the ONL of the MH had a significant effect on the postoperative BCVA at 6 months. Conclusions: Our results indicate that (OPL_DL) − (ONL_DL) had a similar contribution to preoperative visual acuity in predicting the postoperative visual acuity. This model had a strong performance, suggesting that the preoperative visual acuity and MH characteristics in the OCT images were crucial in forecasting the postoperative visual acuity in IMH patients. Thus, it can be used to classify MH patients into groups with good or poor postoperative visual acuity, and the classification was comparable to that of previous studies using deep learning. [ABSTRACT FROM AUTHOR]

With the emergence and development of application requirements such as data analysis and publishing, it is particularly important to use differential privacy protection technology to provide more reliable, secure, and compliant datasets for research in the field of children's health. This paper focuses on the differential privacy protection of the ultrasound examination health dataset of adolescents in southern Texas from three aspects: differential privacy protection with output perturbation on basic statistics, publication of differential privacy marginal histogram and synthesized data, and a machine learning differential privacy learning algorithm. Firstly, differential privacy protection results with output perturbation show that Laplace and Gaussian mechanisms for numerical data, as well as the exponential mechanism for non-numerical data, can achieve the goal of protecting privacy. The exponential mechanism provides higher privacy protection. Secondly, a differential privacy marginal histogram with four attributes can be obtained with an appropriate privacy budget that approximates the marginal histogram of the original data. In order to publish synthetic data, we construct a synthetic query to obtain the corresponding differential privacy histogram for two attributes. Further, a synthetic dataset can be constructed by following the data distribution of the original dataset and the quality of the synthetic data publication can also be evaluated by the mean square error and error rate. Finally, consider a differential privacy logistic regression model under machine learning to predict whether children have fatty liver in binary classification tasks. The experimental results show that the model combined with quadratic perturbation has better accuracy and privacy protection. This paper can provide differential privacy protection models under different demands, which provides important data release and analysis options for data managers and research organizations, in addition to enriching the research on child health data releasing and mining. [ABSTRACT FROM AUTHOR]

Analysis of integrated data often requires record linkage in order to join together the data residing in separate sources. In case linkage errors cannot be avoided, due to the lack a unique identity key that can be used to link the records unequivocally, standard statistical techniques may produce misleading inference if the linked data are treated as if they were true observations. In this paper, we propose methods for categorical data analysis based on linked data that are not prepared by the analyst, such that neither the match‐key variables nor the unlinked records are available. The adjustment is based on the proportion of false links in the linked file and our approach allows the probabilities of correct linkage to vary across the records without requiring that one is able to estimate this probability for each individual record. It accommodates also the general situation where unmatched records that cannot possibly be correctly linked exist in all the sources. The proposed methods are studied by simulation and applied to real data. [ABSTRACT FROM AUTHOR]

Aim: To develop and internally validate risk prediction models for subsequent clinical deterioration, unplanned ICU admission and death among ward patients following medical emergency team (MET) review. Design: A retrospective cohort study of 1500 patients who remained on a general ward following MET review at an Australian quaternary hospital. Method: Logistic regression was used to model (1) subsequent MET review within 48 h, (2) unplanned ICU admission within 48 h and (3) hospital mortality. Models included demographic, clinical and illness severity variables. Model performance was evaluated using discrimination and calibration with optimism‐corrected bootstrapped estimates. Findings are reported using the TRIPOD guideline for multivariable prediction models for prognosis or diagnosis. There was no patient or public involvement in the development and conduct of this study. Results: Within 48 h of index MET review, 8.3% (n = 125) of patients had a subsequent MET review, 7.2% (n = 108) had an unplanned ICU admission and in‐hospital mortality was 16% (n = 240). From clinically preselected predictors, models retained age, sex, comorbidity, resuscitation limitation, acuity‐dependency profile, MET activation triggers and whether the patient was within 24 h of hospital admission, ICU discharge or surgery. Models for subsequent MET review, unplanned ICU admission, and death had adequate accuracy in development and bootstrapped validation samples. Conclusion: Patients requiring MET review demonstrate complex clinical characteristics and the majority remain on the ward after review for deterioration. A risk score could be used to identify patients at risk of poor outcomes after MET review and support general ward clinical decision‐making. Relevance to clinical practice: Our risk calculator estimates risk for patient outcomes following MET review using clinical data available at the bedside. Future validation and implementation could support evidence‐informed team communication and patient placement decisions. [ABSTRACT FROM AUTHOR]

Understanding farmers' perceptions, attitudes, and adaptation strategies toward climate change is important for developing effective policies that support agricultural development and food security in rural communities. This study aimed to understand climatic trends over the past two decades (2003–2022), farmers' perceptions, and adaptation strategies regarding climate change in Dera Ghazi Khan, Pakistan. The Mann–Kendall trend test identified a significant decrease in average minimum temperature (τ = −0.357, p < 0.05) and an increase in rainfall patterns (τ = 0.337, p < 0.05). A mixed-method approach is employed, utilizing a survey of 180 randomly selected farmers, focus group discussions, and climate data analysis. Ordinal and binary logistic regression models were used to analyze the influence of sociodemographic factors on farmers' perceptions of climate change and their choice of adaptation strategies. The results indicate that farmers primarily rely on religious beliefs/prayers (65.6%) to cope with climate change, followed by seeking off-farm jobs (50%) and changing fertilizer use (42.2%). The result of the binary logistic regression indicates that among the different demographic characteristics, education, and land size significantly influence farmers' decisions in taking adaptation measures against climate change. This study emphasizes the need for climate policies that integrate farmer knowledge with meteorological data and sociodemographic analysis to ensure a sustainable agricultural sector in Pakistan. Further research is necessary to explore more effective adaptation methods. [ABSTRACT FROM AUTHOR]

Objective: This study focused on the investigation of the correlation between dietary retinol intake and rheumatoid arthritis (RA) using the National Health and Nutrition Examination Survey (NHANES) database. Methods: Data from five NHANES cycles from 2003 to 2012 were utilized for this study. Dietary retinol intake was considered as the independent variable, and RA was the dependent variable. A weighted logistic regression method was applied to construct the relational model of the two variables. Stratified analysis without adjusting for confounding factors and subgroup analysis with confounding factors adjusted were conducted to explore the association between dietary retinol intake and RA. The optimal intake of dietary retinol was determined by the restricted cubic splines (RCS) analysis. Results: 22,971 samples were included in this study. The weighted logistic regression model was employed to construct the relational model of dietary retinol intake and RA (OR: 0.95, 95% CI: 0.91–0.99, p = 0.019). Stratified analysis displayed a great influence on the relational model exerted by the interaction between gender and retinol intake (p for interaction = 0.014). A significant association between retinol intake and RA was also indicated in the model adjusted for demographic characteristics (OR: 0.95, 95% CI: 0.90-1.00, p = 0.029). Subgroup analysis by gender showed that in the female population, unadjusted model (OR: 0.90, 95% CI: 0.84–0.96, p = 0.002), model adjusted for demographic characteristics only (OR: 0.89, 95% CI: 0.83–0.96, p = 0.002), and model adjusted for all confounding factors (OR: 0.91, 95% CI: 0.85–0.99, p = 0.019) indicated dietary retinol intake as a protective factor against RA. RCS analysis demonstrated that in the female population, regardless of the model used (Crude, Model I, and Model II), an intake of dietary retinol > 354.86 mcg was associated with RA disease reduction (OR < 1.0, p-non-linear < 0.05, p-overall < 0.05). Conclusion: Increased dietary retinol intake was associated with RA disease reduction, particularly in the female population. Women are recommended to increase their dietary retinol intake (> 354.86 mcg) to reduce the risk of RA. [ABSTRACT FROM AUTHOR]

Background: Prediction models are often externally validated with data from a single study or cohort. However, the interpretation of performance estimates obtained with single-study external validation is not as straightforward as assumed. We aimed to illustrate this by conducting a large number of external validations of a prediction model for functional outcome in subarachnoid hemorrhage (SAH) patients. Methods: We used data from the Subarachnoid Hemorrhage International Trialists (SAHIT) data repository (n = 11,931, 14 studies) to refit the SAHIT model for predicting a dichotomous functional outcome (favorable versus unfavorable), with the (extended) Glasgow Outcome Scale or modified Rankin Scale score, at a minimum of three months after discharge. We performed leave-one-cluster-out cross-validation to mimic the process of multiple single-study external validations. Each study represented one cluster. In each of these validations, we assessed discrimination with Harrell's c-statistic and calibration with calibration plots, the intercepts, and the slopes. We used random effects meta-analysis to obtain the (reference) mean performance estimates and between-study heterogeneity (I2-statistic). The influence of case-mix variation on discriminative performance was assessed with the model-based c-statistic and we fitted a "membership model" to obtain a gross estimate of transportability. Results: Across 14 single-study external validations, model performance was highly variable. The mean c-statistic was 0.74 (95%CI 0.70–0.78, range 0.52–0.84, I2 = 0.92), the mean intercept was -0.06 (95%CI -0.37–0.24, range -1.40–0.75, I2 = 0.97), and the mean slope was 0.96 (95%CI 0.78–1.13, range 0.53–1.31, I2 = 0.90). The decrease in discriminative performance was attributable to case-mix variation, between-study heterogeneity, or a combination of both. Incidentally, we observed poor generalizability or transportability of the model. Conclusions: We demonstrate two potential pitfalls in the interpretation of model performance with single-study external validation. With single-study external validation. (1) model performance is highly variable and depends on the choice of validation data and (2) no insight is provided into generalizability or transportability of the model that is needed to guide local implementation. As such, a single single-study external validation can easily be misinterpreted and lead to a false appreciation of the clinical prediction model. Cross-validation is better equipped to address these pitfalls. [ABSTRACT FROM AUTHOR]

Background: Erectile dysfunction (ED) is a very common condition among adult men and its prevalence increases with age. The ankle-brachial blood pressure index (ABPI) is a noninvasive tool used to assess peripheral vascular disease (PAD) and vascular stiffness. However, the association between ABPI and ED is unclear. We aimed to explore the association between ABPI and ED in the US population. Methods: Our study used data from two separate National Health and Nutrition Examination Survey (NHANES) datasets (2001-2002 and 2003-2004). Surveyweighted logistic regression models were used to explore the association between ABPI as a continuous variable and quartiles with ED. We further assessed the association between ABPI and ED using restricted cubic regression while selecting ABPI thresholds using two-piecewise Cox regression models. In addition, we performed subgroup analyses stratified by BMI, race, marital status, diabetes, and hypertension. Main outcome measure: ABPI was calculated by dividing the mean systolic blood pressure at the ankle by the mean systolic blood pressure at the arm. Results: Finally, 2089 participants were enrolled in this study, including 750 (35.90%) ED patients and 1339 (64.10%) participants without ED. After adjusting for all confounding covariates, logistic regression analyses showed a significant association between ABPI and ED (OR=0.19; 95% CI, 0.06-0.56, P=0.01); with ABPI as a categorical variable, compared with the lowest quartile, the OR and 95% CI for the second quartile were 0.58 (0.34-0.97; P = 0.04). Besides, splines indicated that there was an L-shaped relationship between ABPI levels and the risk of ED. Piecewise Cox regression demonstrated the inflection point at 1.14, below which the OR for ED was 0.06 (0.02-0.20; P < 0.001), and above which the OR was 2.79 (0.17-4.53; P = 0.469). Conclusion: In our study, lower ABPI was independently associated with ED risk. In addition, the lowest ABPI level associated with ED risk was 1.14, below this level, lower ABPI was associated with higher ED risk. [ABSTRACT FROM AUTHOR]

ABSTRACT Preclinical studies are broad and can encompass cellular research, animal trials, and small human trials. Preclinical studies tend to be exploratory and have smaller datasets that often consist of biomarker data. Logistic regression is typically the model of choice for modeling a binary outcome with explanatory variables such as genetic, imaging, and clinical data. Small preclinical studies can have challenging data that may include a complete separation or quasi‐complete separation issue that will result in logistic regression inflated coefficient estimates and standard errors. Penalized regression approaches such as Firth's logistic regression are a solution to reduce the bias in the estimates. In this tutorial, a number of examples with separation (complete or quasi‐complete) are illustrated and the results from both logistic regression and Firth's logistic regression are compared to demonstrate the inflated estimates from the standard logistic regression model and bias‐reduction of the estimates from the penalized Firth's approach. R code and datasets are provided in the supplement. [ABSTRACT FROM AUTHOR]

Objective: Our objective is to create an effective ensemble tool that can accurately predict MEFV gene variants and determine the threshold value for pathogenicity based on the optimal distribution. Methods: First, we extracted a dataset from the Infevers database [https://infevers.umai-montpellier.fr/web/search.php?n=1]. Second, we merged the variant classification into 2 categories: likely benign and likely pathogenic. Third, we implemented our high-sensitivity model to obtain disease-causing variants. In the 4 steps, we implemented curve estimation analysis to determine which curve was fitting our variant distribution. We implemented the receiver operating curve after the curve estimation analysis to find suitable in silico tool models for logistic regression. Repeated outlier detection analysis was performed in the fifth step until no outliers were detected. Ensemble tree-based machine-learning models were used to test a statistical model in the final step. Results: When outliers were taken out, the Revel and BayesDel algorithms both had much higher ROCAUC scores (0.982 [0.967-0.998], P < .001 for the combined model; 0.982 [0.967-0.998], P < .001 for Revel; and 0.933 [0.889-0.977], P < .001 for BayesDel). AdaBoost was the most accurate machine learning model, with 0.982 ROACUAC scores. Conclusion: Our study revealed that the implementation of outlier and anomaly detection techniques can enhance the accuracy of statistical models and yield more precise outcomes in machine learning datasets. [ABSTRACT FROM AUTHOR]

Objective To explore the predictive value of machine learning and logistic regression for depression in disabled elderly people, and to analyze the independent risk factors for depression in disabled elderly people. Methods Based on the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS) data and the 2018 China Health and Retirement Longitudinal Study (CHARLS) data, 2 277 disabled elderly individuals aged 65 and above from CLHLS and 476 from CHARLS were included in this study after excluding missing and outlier values. The disabled elderly individuals selected from CLHLS were randomly divided into training and internal validation sets in a 6:4 ratio, with CHARLS serving as the external validation set. Single-factor analysis, LASSO regression, and multiple -factor logistic regression were used to identify the independent risk factors for depression in disabled elderly people, which were then used to construct machine learning and logistic regression risk prediction models. Results Self-rated health, sleep duration, social interaction, cognition, and place of residence were identified as independent risk factors for depression in disabled elderly people. The XGBoost model demonstrated the best comprehensive performance, with an area under the curve (AUG), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, recall, precision, and Brier-score of 0.74, 0.68, 0.72, 0.62, 0.69, 0.65, 0.69, 0.73, 0.20 in the training set, 0.76, 0.70, 0.75, 0.63, 0.72, 0.66: 0.72, 0.75, 0.19 in the internal validation set, and 0.76, 0.74, 0.86, 0.50, 0.77, 0.65, 0.77, 0.87, 0.17 in the external validation set. Conclusion The depression risk prediction model for disabled elderly people constructed based on tlie XGBoost machine learning metliod exhibits good predictive performance and can provide reference for interventions targeting depression in disabled elderly people. [ABSTRACT FROM AUTHOR]

Objective To analyze risk factors for occupational blood-borne exposures among healthcare workers, providing a basis for timely and targeted interventions to prevent occupational blood-borne exposure. Methods The study included 103 healthcare workers who reported occupational blood-borne exposures from January 2016 to June 2023 at a tertiary hospital in Hulunbuir as the exposure group and 625 healthcare workers from the same shifts who did not experience occupational exposures as the control group. Relevant information was collected for all subjects. Random Forest algorithms and multivariate logistic regression analysis were used to construct predictive models for occupational blood-borne exposure among healthcare workers. Results Among the 103 blood-borne exposure cases, the main exposure site was the hand in 89 cases (86.41%); the main mode of exposure was sharp force injury in 83 cases (80.58%); and the predominant exposure source was hepatitis B virus (HBV) in 65 cases (63.11%). Sixty-five cases (63.11%) were reported immediately after exposure, while 24 cases (23.30%) were reported within 12 hours. Logistic regression analysis revealed that compared to healthcare workers aged 30 years or more, likelihood of occupational blood-borne exposure increased to 4.142 times for those under 30 years (P < 0.05) ; compared with 5 or more years, the likelihood of occupational blood-borne exposure increased to 1.696 times for healthcare workers with less than 5 years of service (P < 0.05) the likelihood of occupational blood-borne exposure increased to 5.989 times for junior-title healthcare workers compared to intermediate and higher- level titles (P < 0.05); healthcare workers who attended occupational protection training only once per year were up to 1.864 times more likely to have occupational blood-borne exposure compared to those who participated multiple times annually (P < 0.05); the likelihood of occupational blood-borne exposure increased to 2.205 times for healthcare workers in the departmental categories of emergency, critical care, and operating room compared to those in other departments (P < 0.05). The Random Forest algorithm identified the top 6 influential factors in order of importance as professional title, age, years of work experience, annual training frequency of occupational protection, occupation type, and education level. The Random Forest predictive model demonstrated higher accuracy, precision, recall, and FI score (the reconciled mean of precision and recall) compared to the logistic regression model. The area under the receiver operating characteristic curve (AUC) for the Random Forest model was 0.829 (P < 0.001) which was also higher than that of the logistic regression model of 0.818 (P < 0.001). Conclusions The Random Forest model showed superior predictive performance for occupational blood-borne exposures, while the logistic regression model provided more intuitive analytical results. The combined use of both models can further enhance prediction accuracy. Enhanced training should be provided for high-risk groups, including younger healthcare workers, those with less work experience, and those with lower professional titles. Standardized measures should be implemented to prevent blood-borne exposure among healthcare workers. [ABSTRACT FROM AUTHOR]

The detection performance of radar is significantly impaired by active jamming and mutual interference from other radars. This paper proposes a radio signal modulation recognition method to accurately recognize these signals, which helps in the jamming cancellation decisions. Based on the ensemble learning stacking algorithm improved by meta-feature enhancement, the proposed method adopts random forests, K-nearest neighbors, and Gaussian naive Bayes as the base-learners, with logistic regression serving as the meta-learner. It takes the multi-domain features of signals as input, which include time-domain features including fuzzy entropy, slope entropy, and Hjorth parameters; frequency-domain features, including spectral entropy; and fractal-domain features, including fractal dimension. The simulation experiment, including seven common signal types of radar and active jamming, was performed for the effectiveness validation and performance evaluation. Results proved the proposed method's performance superiority to other classification methods, as well as its ability to meet the requirements of low signal-to-noise ratio and few-shot learning. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we survey the recent development of inversion‐free Newton's method, which directly avoids computing the inversion of Hessian, and demonstrate its applications in estimating parameters of models such as linear and logistic regression. A detailed review of existing methodology is provided, along with comparisons of various competing algorithms. We provide numerical examples that highlight some deficiencies of existing approaches, and demonstrate how the inversion‐free methods can improve performance. Motivated by recent works in literature, we provide a unified subsampling framework that can be combined with the inversion‐free Newton's method to estimate model parameters including those of linear and logistic regression. Numerical examples are provided for illustration. [ABSTRACT FROM AUTHOR]

Transfer learning, as a machine learning approach enhancing model generalization across different domains, has extensive applications in various fields. However, the risk of privacy leakage remains a crucial consideration during the transfer learning process. Differential privacy, with its rigorous mathematical foundation, has been proven to offer consistent and robust privacy protection. This study delves into the logistic regression transfer learning problem supported by differential privacy. In cases where transferable sources are known, we propose a two-step transfer learning algorithm. For scenarios with unknown transferable sources, a non-algorithmic, cross-validation-based transferable source detection method is introduced, to mitigate adverse effects from non-informative sources. The effectiveness of the proposed algorithm is validated through simulations and experiments with real-world data. [ABSTRACT FROM AUTHOR]

Large amounts of co-seismic landslides provide abundant debris sources following a strong earthquake and are prone to initiate and generate debris flow under heavy rainfall. Assessing the susceptibility they pose and what drives their movement in the years following the mainshock has not yet been attempted, primarily because multitemporal debris flow inventories are lacking. This study conducted statistical analyses using the multitemporal debris flow inventory (2008–2021) following the 2008 Wenchuan earthquake and a set of conditional factors (debris source, terrain, and hydrology). The dynamic susceptibility evaluation model was created using logistic regression. The temporal evolution of these factors affecting debris flow runout was explained. Our findings suggest that topography variables grew rapidly after the earthquake, while debris source factors dominated for seven years after the disaster before declining progressively to zero. These findings may have significant consequences for traditional susceptibility assessment models in regions where co-seismic landslides are the dominant debris flow source. [ABSTRACT FROM AUTHOR]

Objective: Presenteeism occurs when employees attend work despite experiencing problems and ill-health that require sick leave. This study examined whether presenteeism worsened following COVID-19 infection. Methods: We used the DeSC, a large health insurance claims database. Participants were 9241 individuals who responded to questionnaires at baseline (June 2020) and 6 months later, had been continuously insured for at least 6 months prior to baseline, and reported being employed. Propensity score matching was performed. Adjusted multiple logistic regression was used to estimate odds ratios and 95% confidence intervals of worsening presenteeism from baseline according to COVID-19 infection compared with noninfection. Results: Conditional logistic regression analysis showed that the adjusted OR for presenteeism in the COVID-19-infected group was 1.555 (95% confidence interval, 1.086-2.225). Conclusions: The findings suggest that COVID-19 infection affected worsening of presenteeism. [ABSTRACT FROM AUTHOR]

Potato virus Y (PVY) is a major tobacco (Nicotiana tabacum L.) pathogen that causes severe crop losses. We studied the influence of meteorological factors and a population of twelve aphid species on the development of PVY in field-grown tobacco from 1996 to 2010 in Poland. Three PVY-susceptible tobacco varieties were used in the study. The mean virus incidence ranged from 18% in 2010 to almost 99% in 1996, 2004, and 2009. For determining the relationship between tobacco plant infection and meteorological conditions and aphid populations, logistic regression analysis was used. It was found that the probability of PVY infection is significantly dependent on the average air temperature, relative humidity, number of days with an average temperature of at least 25 °C, and the abundance of Aphis fabae and Brachycaudus helichrysi. The probability of infection of tobacco plants with potato virus Y decreased with increasing air temperature and relative humidity. In addition, with each subsequent day with a temperature of at least 25 °C, the risk of infection decreased by 24%. Furthermore, it was often observed that high populations of Aphis fabae and Brachycaudus helichrysi were associated with a high incidence of virus infection in tobacco plants. [ABSTRACT FROM AUTHOR]

Going concern of entity is of much concern to stakeholders, especially investors of financial institutions since the failure of financial institutions affects a wide range of sectors. The study sought to develop a model to predict going concern of financial institutions. The data were collected from the financial statements of selected banks in Ghana and Nigeria. The binary logistic regression model was employed as the data analysis technique. The model used financial ratios as predictors of going concern. The result revealed that working capital and earnings before interest and tax on total assets are crucial financial predictors for going concern in financial institutions. The implication is that management should maintain adequate working capital and improve upon its earning capacity to sustain the operations of financial institutions. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate the association between dietary vitamin C intake and depression in adults. This cross-sectional study utilized data from the National Health and Nutrition Examination Survey (NHANES) during 2005 to 2020. Logistic regressions and restricted cubic spline (RCS) regression were used to assess the association between dietary vitamin C intake and depression. Additionally, we performed stratified and sensitivity analyses to evaluate the stability of the results. This study included 38,157 participants, with 3448 (9.04 %) of them experiencing depression. The vitamin C intake was negatively associated with depression after adjusting for all covariates (OR = 0.91, 95%CI: 0.88–0.94, P < 0.001). Similar inverse associations were observed when vitamin C intake was transformed into categorical variables. Individuals in higher quartiles of dietary vitamin C intake (Q2, Q3, and Q4) had lower odds ratios (ORs) compared to those in the lowest quartile (Q1), as indicated by adjusted ORs of 0.78 (95 % CI: 0.71–0.87, P < 0.001), 0.74 (95 % CI: 0.67–0.82, P < 0.001), and 0.73 (95 % CI: 0.65–0.81, P < 0.001), respectively. The RCS analysis found an L-shaped nonlinear relationship between dietary vitamin C intake and depression, after adjusting for all covariates (P for non-linearity<0.001). Consumption of vitamin C was inversely associated with depression (OR = 0.994, 95%CI: 0.993–0.996, P < 0.001) for intakes below 93.61 mg, but there was no association between dietary vitamin C intake and depression (P = 0.980) for intakes of 93.61 mg or higher. The inverse associations between vitamin C intake and depression remained robust in stratified and sensitivity analyses. This study was a cross-sectional study, and therefore unable to establish a causal relationship between dietary vitamin C intake and depression. We are unable to fully eliminate the confounding effects resulted from other unmeasured or unknown factors. The study revealed a negative association between dietary vitamin C intake and depression, as well as an L-shaped nonlinear relationship between vitamin C intake and depression. • This cross-sectional study was conducted using data from the NHANES during 2005 to 2020. Logistic regression models and restricted cubic spline (RCS) regression were used to assess the association between dietary vitamin C intake and the risk of depression. Additionally, we performed stratified and sensitivity analyses to evaluate the stability of the results. • The study revealed a negative association between dietary vitamin C intake and depression, as well as a non-linear relationship in the form of an L-shaped curve between vitamin C intake and the risk of depression. [ABSTRACT FROM AUTHOR]

In this article, we develop an innovative, robust method for jointly analyzing longitudinal count and binary responses. The method is useful for bounding the influence of potential outliers in the data when estimating the model parameters. We use a log‐linear model for the count response and a logistic regression model for the binary response, where the two response processes are linked through a set of association parameters. The asymptotic properties of the robust estimators are briefly studied. The empirical properties of the estimators are studied based on simulations. The study shows that the proposed estimators are approximately unbiased and also efficient when fitting a joint model to data contaminated with outliers. We also apply the proposed method to some real longitudinal survey data obtained from a health study. [ABSTRACT FROM AUTHOR]

A wearable textile bra-tenna system based on dual-polarization sensors for breast cancer (BC) detection is presented in this paper. The core concept behind our work is to investigate which type of polarization is most effective for BC detection, using the combination of orthogonal polarization signals with machine learning (ML) techniques to enhance detection accuracy. The bra-tenna sensors have a bandwidth ranging from 2–12 GHz. To complement the proposed system, detection based on machine learning algorithms (MLAs) is developed and tested to enhance its functionality. Using scattered signals at different polarizations, the bra-tenna system uses MLAs to predict BC in its early stages. Classification techniques are highly effective for data classification, especially in the biomedical field. Two scenarios are considered: Scenario 1, where the system detects a tumor or non-tumor, and Scenario 2, where the system detects three classes of one, two, and non-tumors. This confirms that MLAs can detect tumors as small as 10 mm. ML techniques, including eight algorithms such as the Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Methods (GBMs), Decision Tree (DT) classifier, Ada Boost (AD), CatBoost, Extreme Gradient Boosting (XG Boost), and Logistic Regression (LR), are applied to this balanced dataset. For optimal analysis of the BC, a performance evaluation is performed. Notably, SVM achieves outstanding performance in both scenarios, with metrics such as its F1 score, recall, accuracy, receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), and precision all exceeding 90%, helping doctors to effectively investigate BC. Furthermore, the Horizontal-Horizontal (HH) sensor configuration achieved the highest accuracy of 98% and 99% for SVMs in the two scenarios, respectively. [ABSTRACT FROM AUTHOR]

Darkness is a barrier to cycling because the risk of an accident is higher and the feeling of social safety is lower. Factors influencing the choice to cycle (e.g., socio-demographic factors) have already been studied extensively, but rarely in relation to darkness. To implement strategies for increasing bicycle use (also when it is dark), we need to know what decision factors play a role for people to cycle or not when it is dark. A mixed method approach is adopted; applying a logistic regression using data of 842 surveyed cyclists in the province of East Flanders (Belgium), supplemented a qualitative analysis using 26 in-depth semi-structured interviews. Results show that knowing and using a route frequently has a positive effect on the likelihood of cycling in the dark. Elderly (and retirees) are less likely to cycle in the dark compared to youth and young adults. The latter group (ranging between 11 and 17 years old) often cycle to school while it is still dark (either or not accompanied by a parent). Whether or not one cycles alone or in group has a lower or higher propensity to cycle during the dark. People with a racing bike often cycle in groups and are less likely to cycle in the dark. Recreative and sportive cycle tours are mostly done during the day. Cyclists with an e-bike and speed pedelec have a higher likelihood to cycle in the dark compared to those who cycle on a regular bike. Women have 84% lower odds to cycle in the dark compared to men because they perceive darkness as less socially safe. Lastly, living in a rural environment has a positive impact on the odds to cycle in the dark compared to more urban environments. Establishing a cycling culture with inclusive infrastructure is essential for addressing the diverse needs of vulnerable groups and ensuring the provision of safe routes. A holistic approach is critical to effectively integrate these elements into urban planning and transport policies. • Darkness is a barrier to cycling due to a lower (sense of) safety. • More insights are required to organize effective strategies for increasing bike use. • Knowing and using a route frequently has a positive impact on cycling in the dark. • Young adults, men and electric bike owners have higher odds of cycling in the dark. • Policy interventions should focus on the actual needs of the intended target groups. [ABSTRACT FROM AUTHOR]