Large-Scale Bayesian Logistic Regression for Text Categorization

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.

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Landslide susceptibility mapping along road corridors in the Indian Himalayas using Bayesian logistic regression models

Geomorphology ◽

10.1016/j.geomorph.2012.08.004 ◽

2012 ◽

Vol 179 ◽

pp. 116-125 ◽

Cited By ~ 65

Author(s):

Iswar Das ◽

Alfred Stein ◽

Norman Kerle ◽

Vinay K. Dadhwal

Keyword(s):

Logistic Regression ◽

Landslide Susceptibility ◽

Regression Models ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Logistic Regression Models ◽

Road Corridors ◽

Bayesian Logistic Regression ◽

Indian Himalayas

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

Journal of Burn Care & Research ◽

10.1093/jbcr/irab032.049 ◽

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.

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Logistic Regression Bias Correction for Large Scale Data with Rare Events

Advanced Data Mining and Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-642-53917-6_12 ◽

2013 ◽

pp. 133-144 ◽

Cited By ~ 1

Author(s):

Zhen Qiu ◽

Hongyan Li ◽

Hanchen Su ◽

Gaoyan Ou ◽

Tengjiao Wang

Keyword(s):

Logistic Regression ◽

Bias Correction ◽

Large Scale ◽

Rare Events ◽

Large Scale Data ◽

Scale Data

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The Impact of Extraversion and Sensation Seeking on Tourist Role

Social Behavior and Personality An International Journal ◽

10.2224/sbp.2015.43.1.75 ◽

2015 ◽

Vol 43 (1) ◽

pp. 75-84 ◽

Cited By ~ 6

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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