Application of Logistic Regression for Signal Detection and Risk Assessment of Macrolide-Associated Torsade De Pointes

Aim: Metabolic syndrome (MS) screening is essential for the early detection of the occupational population. This study aimed to screen out biomarkers related to MS and establish a risk assessment and prediction model for the routine physical examination of an occupational population.Methods: The least absolute shrinkage and selection operator (Lasso) regression algorithm of machine learning was used to screen biomarkers related to MS. Then, the accuracy of the logistic regression model was further verified based on the Lasso regression algorithm. The areas under the receiving operating characteristic curves were used to evaluate the selection accuracy of biomarkers in identifying MS subjects with risk. The screened biomarkers were used to establish a logistic regression model and calculate the odds ratio (OR) of the corresponding biomarkers. A nomogram risk prediction model was established based on the selected biomarkers, and the consistency index (C-index) and calibration curve were derived.Results: A total of 2,844 occupational workers were included, and 10 biomarkers related to MS were screened. The number of non-MS cases was 2,189 and that of MS was 655. The area under the curve (AUC) value for non-Lasso and Lasso logistic regression was 0.652 and 0.907, respectively. The established risk assessment model revealed that the main risk biomarkers were absolute basophil count (OR: 3.38, CI:1.05–6.85), platelet packed volume (OR: 2.63, CI:2.31–3.79), leukocyte count (OR: 2.01, CI:1.79–2.19), red blood cell count (OR: 1.99, CI:1.80–2.71), and alanine aminotransferase level (OR: 1.53, CI:1.12–1.98). Furthermore, favorable results with C-indexes (0.840) and calibration curves closer to ideal curves indicated the accurate predictive ability of this nomogram.Conclusions: The risk assessment model based on the Lasso logistic regression algorithm helped identify MS with high accuracy in physically examining an occupational population.

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Fire Risk Assessment Using Neural Network and Logistic Regression

Journal of the Indian Society of Remote Sensing ◽

10.1007/s12524-016-0557-6 ◽

2016 ◽

Vol 44 (6) ◽

pp. 885-894 ◽

Cited By ~ 20

Author(s):

Y. Jafari Goldarag ◽

Ali Mohammadzadeh ◽

A. S. Ardakani

Keyword(s):

Neural Network ◽

Risk Assessment ◽

Logistic Regression ◽

Fire Risk ◽

Fire Risk Assessment

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Testing the Static-99R as a Global Screen for Risk of Sex Crime Recidivism in a Norwegian Routine Sample

Sexual Abuse ◽

10.1177/1079063220951194 ◽

2020 ◽

pp. 107906322095119

Author(s):

Ingeborg Jenssen Sandbukt ◽

Torbjørn Skardhamar ◽

Ragnar Kristoffersen ◽

Christine Friestad

Keyword(s):

Risk Assessment ◽

Logistic Regression ◽

Random Forest ◽

Predictive Accuracy ◽

Proportional Hazard ◽

Random Forest Classification ◽

Forest Classification ◽

Sex Offense ◽

Observation Period ◽

Sex Crime

The Static-99R has been recommended for use as a first global screen for sorting out sex-convicted persons who are in need of further risk assessment. This study investigated the Static-99R’s predictive validity based on a nonselected Norwegian sample ( n = 858) of persons released from prison after having served a sex crime sentence. After a mean observation period of 2,183 days, 3.4% ( n = 29) had recidivated to a new sex offense. A higher number of recidivists were found among those with higher Static-99R total scores. The predictive contribution from each of the ten Static-99R risk items was investigated using standard logistic regression, proportional hazard regression, and random forest classification algorithm. The overall results indicate that the Static-99R is relevant as a risk screen in a Norwegian context, providing similar results concerning predictive accuracy as previous studies.

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Validation of A Fatigue Failure-based Risk Assessment Tool For Distal Upper Extremity MSDs

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1541931213601707 ◽

2017 ◽

Vol 61 (1) ◽

pp. 911-913

Author(s):

Sean Gallagher ◽

Mark C. Schall ◽

Richard F. Sesek ◽

Rong Huangfu

Keyword(s):

Risk Assessment ◽

Logistic Regression ◽

Upper Extremity ◽

Fatigue Failure ◽

Assessment Tool ◽

Regression Analyses ◽

Automotive Manufacturing ◽

Manufacturing Plants ◽

Failure Theory ◽

Epidemiological Database

This paper presents a new practitioner-friendly distal upper extremity tool (DUET) based on fatigue failure theory. The tool requires only assessment of force exertion (using the OMNI-RES scale) and the number of repetitions performed to derive DUE risk. Validation of this tool was performed against six separate DUE outcomes from an epidemiological database involving data obtained from 1,022 participants across 738 jobs from several automotive manufacturing plants. The DUET Cumulative Damage (CD) measure explained between 79-95% of the deviance for the six DUE outcomes in logistic regression analyses.

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Vibrational risk assessment as a signal detection problem: Escape hatching of red‐eyed treefrog eggs

The Journal of the Acoustical Society of America ◽

10.1121/1.4781915 ◽

2007 ◽

Vol 121 (5) ◽

pp. 3080-3080

Author(s):

Karen M. Warkentin ◽

Michael S. Caldwell ◽

J. Gregory McDaniel

Keyword(s):

Risk Assessment ◽

Signal Detection ◽

Detection Problem

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Fire Risk Assessment Models Using Statistical Machine Learning and Optimized Risk Indexing

Applied Sciences ◽

10.3390/app10124199 ◽

2020 ◽

Vol 10 (12) ◽

pp. 4199

Author(s):

Myoung-Young Choi ◽

Sunghae Jun

Keyword(s):

Machine Learning ◽

Risk Assessment ◽

Logistic Regression ◽

Prediction Model ◽

Fire Risk ◽

Fire Occurrence ◽

Statistical Machine Learning ◽

Fire Insurance ◽

Comparison Results ◽

Fire Risk Assessment

It is very difficult for us to accurately predict occurrence of a fire. But, this is very important to protect human life and property. So, we study fire hazard prediction and evaluation methods to cope with fire risks. In this paper, we propose three models based on statistical machine learning and optimized risk indexing for fire risk assessment. We build logistic regression, deep neural networks (DNN) and fire risk indexing models, and verify performances between proposed and traditional models using real investigated data related to fire occurrence in Korea. In general, fire prediction models currently in use do not provide satisfactory levels of accuracy. The reason for this result is that the factors affecting fire occurrence are very diverse and frequency of fire occurrence is very sparse. To improve accuracy of fire occurrence, we first build logistic regression and DNN models. In addition, we construct a fire risk indexing model for a more improved model of fire prediction. To illustrate comparison results between our research models and current fire prediction model, we use real fire data investigated in Korea between 2011 to 2017. From the experimental results of this paper, we can confirm that accuracy of prediction by the proposed method is superior to the existing fire occurrence prediction model. Therefore, we expect the proposed model to contribute to evaluating the possibility of fire risk in buildings and factories in the field of fire insurance and to calculate the fire insurance premium.

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Clinical risk factors and predictive tool of bacteremia in patients with cirrhosis

Journal of International Medical Research ◽

10.1177/0300060520919220 ◽

2020 ◽

Vol 48 (5) ◽

pp. 030006052091922

Author(s):

Qiao Yang ◽

Xian Zhong Jiang ◽

Yong Fen Zhu ◽

Fang Fang Lv

Keyword(s):

Risk Factors ◽

Risk Assessment ◽

Logistic Regression ◽

Cross Validation ◽

Bloodstream Infections ◽

Roc Curves ◽

Predictive Tool ◽

Reactive Protein ◽

Significant Difference ◽

Fold Cross Validation

Objective We aimed to analyze the risk factors and to establish a predictive tool for the occurrence of bloodstream infections (BSI) in patients with cirrhosis. Methods A total of 2888 patients with cirrhosis were retrospectively included. Multivariate analysis for risk factors of BSI were tested using logistic regression. Multivariate logistic regression was validated using five-fold cross-validation. Results Variables that were independently associated with incidence of BSI were white blood cell count (odds ratio [OR] = 1.094, 95% confidence interval [CI] 1.063–1.127)], C-reactive protein (OR = 1.005, 95% CI 1.002–1.008), total bilirubin (OR = 1.003, 95% CI 1.002–1.004), and previous antimicrobial exposure (OR = 4.556, 95% CI 3.369–6.160); albumin (OR = 0.904, 95% CI 0.883–0.926), platelet count (OR = 0.996, 95% CI 0.994–0.998), and serum creatinine (OR = 0.989, 95% CI 0.985–0.994) were associated with lower odds of BSI. The area under receiver operating characteristic (ROC) curve of the risk assessment scale was 0.850, and its sensitivity and specificity were 0.762 and 0.801, respectively. There was no significant difference between the ROC curves of cross-validation and risk assessment. Conclusions We developed a predictive tool for BSI in patients with cirrhosis, which could help with early identification of such episodes at admission, to improve outcome in these patients.

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