scholarly journals Large-Scale Bayesian Logistic Regression for Text Categorization

Technometrics ◽  
2007 ◽  
Vol 49 (3) ◽  
pp. 291-304 ◽  
Author(s):  
Alexander Genkin ◽  
David D Lewis ◽  
David Madigan
Keyword(s):  
Logistic Regression ◽  
Text Categorization ◽  
Large Scale ◽  
Bayesian Logistic Regression

scholarly journals A sparse version of the ridge logistic regression for large-scale text categorization

2011 ◽  
Vol 32 (2) ◽  
pp. 101-106 ◽  
Author(s):  
Sujeevan Aseervatham ◽  
Anestis Antoniadis ◽  
Eric Gaussier ◽  
Michel Burlet ◽  
Yves Denneulin
Keyword(s):  
Logistic Regression ◽  
Text Categorization ◽  
Large Scale

scholarly journals Cross-Sectional Association between Hypercholesterolemia and Knee Pain in the Elderly with Radiographic Knee Osteoarthritis: Data from the Korean National Health and Nutritional Examination Survey

2021 ◽  
Vol 10 (5) ◽  
pp. 933
Author(s):  
Byung Woo Cho ◽  
Du Seong Kim ◽  
Hyuck Min Kwon ◽  
Ick Hwan Yang ◽  
Woo-Suk Lee ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Knee Pain ◽  
Logistic Regression Analysis ◽  
National Health ◽  
Large Scale ◽  
Metabolic Diseases ◽  
The Elderly ◽  
Cross Sectional ◽  
Ordinal Logistic Regression Analysis

Few studies have reported the relationship between knee pain and hypercholesterolemia in the elderly population with osteoarthritis (OA), independent of other variables. The aim of this study was to reveal the association between knee pain and metabolic diseases including hypercholesterolemia using a large-scale cohort. A cross-sectional study was conducted using data from the Korea National Health and the Nutrition Examination Survey (KNHANES-V, VI-1; 2010–2013). Among the subjects aged ≥60 years, 7438 subjects (weighted number estimate = 35,524,307) who replied knee pain item and performed the simple radiographs of knee were enrolled. Using multivariable ordinal logistic regression analysis, variables affecting knee pain were identified, and the odds ratio (OR) was calculated. Of the 35,524,307 subjects, 10,630,836 (29.9%) subjects experienced knee pain. Overall, 20,290,421 subjects (56.3%) had radiographic OA, and 8,119,372 (40.0%) of them complained of knee pain. Multivariable ordinal logistic regression analysis showed that among the metabolic diseases, only hypercholesterolemia was positively correlated with knee pain in the OA group (OR 1.24; 95% Confidence Interval 1.02–1.52, p = 0.033). There were no metabolic diseases correlated with knee pain in the non-OA group. This large-scale study revealed that in the elderly, hypercholesterolemia was positively associated with knee pain independent of body mass index and other metabolic diseases in the OA group, but not in the non-OA group. These results will help in understanding the nature of arthritic pain, and may support the need for exploring the longitudinal associations.

45 Application of Machine Learning Models to Thermal Burn Patient Outcome Predictions in the Aftermath of a Nuclear Event

2021 ◽  
Vol 42 (Supplement_1) ◽  
pp. S33-S34
Author(s):  
Morgan A Taylor ◽  
Randy D Kearns ◽  
Jeffrey E Carter ◽  
Mark H Ebell ◽  
Curt A Harris
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Length Of Stay ◽  
Regression Models ◽  
Large Scale ◽  
Prediction Models ◽  
Burn Patients ◽  
Thermal Burn ◽  
Logistic Regression Models ◽  
Burn Patient

Abstract Introduction A nuclear disaster would generate an unprecedented volume of thermal burn patients from the explosion and subsequent mass fires (Figure 1). Prediction models characterizing outcomes for these patients may better equip healthcare providers and other responders to manage large scale nuclear events. Logistic regression models have traditionally been employed to develop prediction scores for mortality of all burn patients. However, other healthcare disciplines have increasingly transitioned to machine learning (ML) models, which are automatically generated and continually improved, potentially increasing predictive accuracy. Preliminary research suggests ML models can predict burn patient mortality more accurately than commonly used prediction scores. The purpose of this study is to examine the efficacy of various ML methods in assessing thermal burn patient mortality and length of stay in burn centers. Methods This retrospective study identified patients with fire/flame burn etiologies in the National Burn Repository between the years 2009 – 2018. Patients were randomly partitioned into a 67%/33% split for training and validation. A random forest model (RF) and an artificial neural network (ANN) were then constructed for each outcome, mortality and length of stay. These models were then compared to logistic regression models and previously developed prediction tools with similar outcomes using a combination of classification and regression metrics. Results During the study period, 82,404 burn patients with a thermal etiology were identified in the analysis. The ANN models will likely tend to overfit the data, which can be resolved by ending the model training early or adding additional regularization parameters. Further exploration of the advantages and limitations of these models is forthcoming as metric analyses become available. Conclusions In this proof-of-concept study, we anticipate that at least one ML model will predict the targeted outcomes of thermal burn patient mortality and length of stay as judged by the fidelity with which it matches the logistic regression analysis. These advancements can then help disaster preparedness programs consider resource limitations during catastrophic incidents resulting in burn injuries.

The Impact of Extraversion and Sensation Seeking on Tourist Role

2015 ◽  
Vol 43 (1) ◽  
pp. 75-84 ◽  
Author(s):  
Cheng-Yu Li ◽  
Shiao-Yuan Lu ◽  
Bi-Kun Tsai ◽  
Keh-Yuan Yu
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Sensation Seeking ◽  
Large Scale ◽  
Personality Variables ◽  
Tourism Marketing ◽  
Large Scale Survey ◽  
And Behaviors ◽  
The Impact

In recent years, personality variables, such as extraversion and sensation seeking, have been used to investigate tourist preferences and behaviors. For this study, we classified tourist roles into three types: the familiarized mass tourist, the organized mass tourist, and the independent tourist. We investigated the impact of extraversion and sensation seeking on tourist roles in a large-scale survey of Taiwanese citizens (N = 1,249) aged 20 years and older. Using logistic regression analysis, the results indicated that sensation seeking was a significant predictor of tourist role, but extraversion was not. Compared to familiarized mass tourists, people who are sensation-seeking are more likely to become independent tourists rather than organized mass tourists. We provide suggestions for tourism marketing.

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