scholarly journals Scaling Multinomial Logistic Regression via Hybrid Parallelism

2019 ◽  
Author(s):  
Parameswaran Raman ◽  
Sriram Srinivasan ◽  
Shin Matsushima ◽  
Xinhua Zhang ◽  
Hyokun Yun ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Multinomial Logistic Regression ◽  
Hybrid Parallelism

Public knowledge, perception and communication behavior surrounding COVID-19 in Malaysia

2020 ◽  
Author(s):  
Khayriyyah Mohd Hanafiah ◽  
Chang Da Wan
Keyword(s):  
Logistic Regression ◽  
Employment Status ◽  
Multinomial Logistic Regression ◽  
Movement Control ◽  
Education Level ◽  
Public Knowledge ◽  
Communication Behavior ◽  
Positive Communication ◽  
The Face ◽  
Control Order

The COVID-19 pandemic is the first to occur in an age of hyperconnectivity. This paper presents results from an online anonymous survey conducted in Malay, English, and Chinese, during the first week of the Movement Control Order in Malaysia (n=1075), which aimed to examine public knowledge, perception and communication behavior in the Malaysian society in the face of a sudden outbreak and social distancing measures. Although the level of public knowledge, risk perception and positive communication behavior surrounding COVID-19 was high, a majority of respondents reported receiving a lot of questionable information. Multinomial logistic regression further identified that responses to different items varied significantly across respondent survey language, gender, age, education level and employment status.

scholarly journals Reduced rank multinomial logistic regression in Markov chains with application to cognitive data

2021 ◽  
Author(s):  
Pei Wang ◽  
Erin L. Abner ◽  
David W. Fardo ◽  
Frederick A. Schmitt ◽  
Gregory A. Jicha ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Markov Chains ◽  
Multinomial Logistic Regression ◽  
Reduced Rank ◽  
Cognitive Data

scholarly journals Association between Social Integration and Face Mask Use Behavior during the SARS-CoV-2 Pandemic in Japan: Results from U-CORONA Study

2021 ◽  
Vol 18 (9) ◽  
pp. 4717
Author(s):  
Nobutoshi Nawa ◽  
Yui Yamaoka ◽  
Yuna Koyama ◽  
Hisaaki Nishimura ◽  
Shiro Sonoda ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Social Network ◽  
Social Integration ◽  
Response Bias ◽  
Multinomial Logistic Regression ◽  
Face Mask ◽  
Neighborhood Association ◽  
Multinomial Logistic Regression Analysis ◽  
Use Behavior

Face mask use is a critical behavior to prevent the spread of SARS-CoV-2. We aimed to evaluate the association between social integration and face mask use during the COVID-19 pandemic in a random sample of households in Utsunomiya City, Greater Tokyo, Japan. Data included 645 adults in the Utsunomiya COVID-19 seROprevalence Neighborhood Association (U-CORONA) study, which was conducted after the first wave of the pandemic, between 14 June 2020 and 5 July 2020, in Utsunomiya City. Social integration before the pandemic was assessed by counting the number of social roles, based on the Cohen’s social network index. Face mask use before and during the pandemic was assessed by questionnaire, and participants were categorized into consistent mask users, new users, and current non-users. Multinomial logistic regression analysis was used to examine the association between lower social integration score and face mask use. To account for possible differential non-response bias, non-response weights were used. Of the 645 participants, 172 (26.7%) were consistent mask users and 460 (71.3%) were new users, while 13 (2.0%) were current non-users. Lower social integration level was positively associated with non-users (RRR: 1.76, 95% CI: 1.10, 2.82). Social integration may be important to promote face mask use.

scholarly journals A comprehensive, contemporary assessment of the association between hepatosteatosis and coronary artery calcium scoring

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
T Heseltine ◽  
SW Murray ◽  
RL Jones ◽  
M Fisher ◽  
B Ruzsics
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Coronary Artery ◽  
Regression Model ◽  
Coronary Artery Calcium ◽  
Logistic Regression Model ◽  
Prediction Models ◽  
Multinomial Logistic Regression ◽  
Cvd Risk ◽  
Male Sex

Abstract Funding Acknowledgements Type of funding sources: None. onbehalf Liverpool Multiparametric Imaging Collaboration Background Coronary artery calcium (CAC) score is a well-established technique for stratifying an individual’s cardiovascular disease (CVD) risk. Several well-established registries have incorporated CAC scoring into CVD risk prediction models to enhance accuracy. Hepatosteatosis (HS) has been shown to be an independent predictor of CVD events and can be measured on non-contrast computed tomography (CT). We sought to undertake a contemporary, comprehensive assessment of the influence of HS on CAC score alongside traditional CVD risk factors. In patients with HS it may be beneficial to offer routine CAC screening to evaluate CVD risk to enhance opportunities for earlier primary prevention strategies. Methods We performed a retrospective, observational analysis at a high-volume cardiac CT centre analysing consecutive CT coronary angiography (CTCA) studies. All patients referred for investigation of chest pain over a 28-month period (June 2014 to November 2016) were included. Patients with established CVD were excluded. The cardiac findings were reported by a cardiologist and retrospectively analysed by two independent radiologists for the presence of HS. Those with CAC of zero and those with CAC greater than zero were compared for demographic and cardiac risks. A multivariate analysis comparing the risk factors was performed to adjust for the presence of established risk factors. A binomial logistic regression model was developed to assess the association between the presence of HS and increasing strata of CAC. Results In total there were 1499 patients referred for CTCA without prior evidence of CVD. The assessment of HS was completed in 1195 (79.7%) and CAC score was performed in 1103 (92.3%). There were 466 with CVD and 637 without CVD. The prevalence of HS was significantly higher in those with CVD versus those without CVD on CTCA (51.3% versus 39.9%, p = 0.007). Male sex (50.7% versus 36.1% p= <0.001), age (59.4 ± 13.7 versus 48.1 ± 13.6, p= <0.001) and diabetes (12.4% versus 6.9%, p = 0.04) were also significantly higher in the CAC group compared to the CAC score of zero. HS was associated with increasing strata of CAC score compared with CAC of zero (CAC score 1-100 OR1.47, p = 0.01, CAC score 101-400 OR:1.68, p = 0.02, CAC score >400 OR 1.42, p = 0.14). This association became non-significant in the highest strata of CAC score. Conclusion We found a significant association between the increasing age, male sex, diabetes and HS with the presence of CAC. HS was also associated with a more severe phenotype of CVD based on the multinomial logistic regression model. Although the association reduced for the highest strata of CAC (CAC score >400) this likely reflects the overall low numbers of patients within this group and is likely a type II error. Based on these findings it may be appropriate to offer routine CVD risk stratification techniques in all those diagnosed with HS.

scholarly journals Predictive CT features for the diagnosis of primary pulmonary mucoepidermoid carcinoma: comparison with squamous cell carcinomas and adenocarcinomas

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiaohua Ban ◽  
Xinping Shen ◽  
Huijun Hu ◽  
Rong Zhang ◽  
Chuanmiao Xie ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Roc Curve ◽  
Multinomial Logistic Regression ◽  
Univariate Analysis ◽  
Ct Imaging ◽  
Squamous Cell Carcinomas ◽  
Curve Analysis ◽  
Imaging Features ◽  
Roc Curve Analysis ◽  
Ct Features

Abstract Background To determine the predictive CT imaging features for diagnosis in patients with primary pulmonary mucoepidermoid carcinomas (PMECs). Materials and methods CT imaging features of 37 patients with primary PMECs, 76 with squamous cell carcinomas (SCCs) and 78 with adenocarcinomas were retrospectively reviewed. The difference of CT features among the PMECs, SCCs and adenocarcinomas was analyzed using univariate analysis, followed by multinomial logistic regression and receiver operating characteristic (ROC) curve analysis. Results CT imaging features including tumor size, location, margin, shape, necrosis and degree of enhancement were significant different among the PMECs, SCCs and adenocarcinomas, as determined by univariate analysis (P < 0.05). Only lesion location, shape, margin and degree of enhancement remained independent factors in multinomial logistic regression analysis. ROC curve analysis showed that the area under curve of the obtained multinomial logistic regression model was 0.805 (95%CI: 0.704–0.906). Conclusion The prediction model derived from location, margin, shape and degree of enhancement can be used for preoperative diagnosis of PMECs.

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