scholarly journals Application of multinomial and ordinal logistic regression to model injury severity of truck crashes, using violation and crash data

2018 ◽  
Vol 26 (4) ◽  
pp. 268-277 ◽  
Author(s):  
Mahdi Rezapour ◽  
Khaled Ksaibati
Keyword(s):  
Logistic Regression ◽  
Injury Severity ◽  
Ordinal Logistic Regression ◽  
Crash Data

scholarly journals Global Sensory Impairment Independently Predicts Decreased Social Function Over Time

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 414-414
Author(s):  
Anna Huang ◽  
Kristen Wroblewski ◽  
Ashwin Kotwal ◽  
Linda Waite ◽  
Martha McClintock ◽  
...  
Keyword(s):  
Mental Health ◽  
Logistic Regression ◽  
Cognitive Function ◽  
Social Engagement ◽  
Social Function ◽  
Ordinal Logistic Regression ◽  
Sensory Function ◽  
Sensory Impairment ◽  
Over Time

Abstract The classical senses (vision, hearing, touch, taste, and smell) play a key role in social function by allowing interaction and communication. We assessed whether sensory impairment across all 5 modalities (global sensory impairment [GSI]) was associated with social function in older adults. Sensory function was measured in 3,005 home-dwelling older U.S. adults at baseline in the National Social Life, Health, and Aging Project and GSI, a validated measure, was calculated. Social network size and kin composition, number of close friends, and social engagement were assessed at baseline and 5- and 10-year follow-up. Ordinal logistic regression and mixed effects ordinal logistic regression analyzed cross-sectional and longitudinal relationships respectively, controlling for demographics, physical/mental health, disability, and cognitive function (at baseline). Adults with worse GSI had smaller networks (β=-0.159, p=0.021), fewer close friends (β=-0.262, p=0.003) and lower engagement (β=-0.252, p=0.006) at baseline, relationships that persisted at 5 and 10 year follow-up. Men, older people, African-Americans, and those with less education, fewer assets, poor mental health, worse cognitive function, and more disability had worse GSI. Men and those with fewer assets, worse cognitive function, and less education had smaller networks and lower engagement. African-American and Hispanic individuals had smaller networks and fewer close friends, but more engagement. Older respondents also had more engagement. In summary, GSI independently predicts smaller social networks, fewer close friends, and lower social engagement over time, suggesting that sensory decline results in decreased social function. Thus, rehabilitating multisensory impairment may be a strategy to enhance social function as people age.

Analysis on application level based on ordinal logistic regression and best of advanced manufacturing technologies (AMT) selection based on fuzzy-TOPSIS integration approach

2021 ◽  
pp. 1-11
Author(s):  
Guilian Wang ◽  
Liyan Zhang ◽  
Jing Guo
Keyword(s):  
Logistic Regression ◽  
Organizational Factors ◽  
Fuzzy Topsis ◽  
Ordinal Logistic Regression ◽  
Industrial Robots ◽  
Policy Implications ◽  
Market Pricing ◽  
Factors Affecting ◽  
Pricing Power ◽  
The Core Competence

This paper try to fully reveal the key factors affecting the the level of AMT application in micro- and small enterprises (MSEs) from its organizational factors by ordinal logistic regression. The results show that MSEs have a relatively high level of AMT application as a whole due to the maturity and cost reduction of basic technologies such as artificial intelligence, digital manufacturing and industrial robots. In this paper we propose manufacturing world analysis at Application using Logistic Regression and best AMT selection using Fuzzy-TOPSIS Integration approach.Considering the influence mechanism of each factor, the important factors that affect the application level of AMT are the enterprise’s market pricing power, the main production types, technical, market and management capabilities, organization development incentives and the interaction with external stakeholders. Based on the results above, the following policy implications are proposed: further expanding the customized production in MSEs to gradually improve the market pricing power, expanding the core competence of enterprises, enhancing the employee autonomy, and strengthening the interaction with industry organizations.

scholarly journals Analysis of Severe Injuries in Crashes Involving Large Trucks Using K-Prototypes Clustering-Based GBDT Model

Safety ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 32
Author(s):  
Syed As-Sadeq Tahfim ◽  
Chen Yan
Keyword(s):  
Injury Severity ◽  
Unobserved Heterogeneity ◽  
The United States ◽  
Clustering Methods ◽  
Severe Injuries ◽  
Traffic Crash ◽  
Crash Data ◽  
Boosted Decision Trees ◽  
The Individual ◽  
Crash Characteristics

The unobserved heterogeneity in traffic crash data hides certain relationships between the contributory factors and injury severity. The literature has been limited in exploring different types of clustering methods for the analysis of the injury severity in crashes involving large trucks. Additionally, the variability of data type in traffic crash data has rarely been addressed. This study explored the application of the k-prototypes clustering method to countermeasure the unobserved heterogeneity in large truck-involved crashes that had occurred in the United States between the period of 2016 to 2019. The study segmented the entire dataset (EDS) into three homogeneous clusters. Four gradient boosted decision trees (GBDT) models were developed on the EDS and individual clusters to predict the injury severity in crashes involving large trucks. The list of input features included crash characteristics, truck characteristics, roadway attributes, time and location of the crash, and environmental factors. Each cluster-based GBDT model was compared with the EDS-based model. Two of the three cluster-based models showed significant improvement in their predicting performances. Additionally, feature analysis using the SHAP (Shapley additive explanations) method identified few new important features in each cluster and showed that some features have a different degree of effects on severe injuries in the individual clusters. The current study concluded that the k-prototypes clustering-based GBDT model is a promising approach to reveal hidden insights, which can be used to improve safety measures, roadway conditions and policies for the prevention of severe injuries in crashes involving large trucks.

Examining Driver Injury Severity in Single-Vehicle Road Departure Crashes Involving Large Trucks

2021 ◽  
pp. 036119812110101
Author(s):  
Mehdi Hosseinpour ◽  
Kirolos Haleem
Keyword(s):  
Rural Areas ◽  
Injury Severity ◽  
Factors Affecting ◽  
Crash Data ◽  
Natural Logarithm ◽  
Concrete Barriers ◽  
Local Roads ◽  
Single Vehicle ◽  
Safety Countermeasures ◽  
Injury Severities

Road departure (RD) crashes are among the most severe crashes that can result in fatal or serious injuries, especially when involving large trucks. Most previous studies neglected to incorporate both roadside and median hazards into large-truck RD crash severity analysis. The objective of this study was to identify the significant factors affecting driver injury severity in single-vehicle RD crashes involving large trucks. A random-parameters ordered probit (RPOP) model was developed using extensive crash data collected on roadways in the state of Kentucky between 2015 and 2019. The RPOP model results showed that the effect of local roadways, the natural logarithm of annual average daily traffic (AADT), the presence of median concrete barriers, cable barrier-involved collisions, and dry surfaces were found to be random across the crash observations. The results also showed that older drivers, ejected drivers, and drivers trapped in their truck were more likely to sustain severe single-vehicle RD crashes. Other variables increasing the probability of driver injury severity have included rural areas, dry road surfaces, higher speed limits, single-unit truck types, principal arterials, overturning-consequences, truck fire occurrence, segments with median concrete barriers, and roadside fixed object strikes. On the other hand, wearing seatbelt, local roads and minor collectors, higher AADT, and hitting median cable barriers were associated with lower injury severities. Potential safety countermeasures from the study findings include installing median cable barriers and flattening steep roadside embankments along those roadway stretches with high history of RD large-truck-related crashes.

Depression as a Risk Factor for Denture Dissatisfaction

2007 ◽  
Vol 86 (9) ◽  
pp. 852-856 ◽  
Author(s):  
M.T. John ◽  
W. Micheelis ◽  
J.G. Steele
Keyword(s):  
Older Adults ◽  
Logistic Regression ◽  
Full Range ◽  
Geriatric Depression Scale ◽  
Depression Scale ◽  
Population Based ◽  
Ordinal Logistic Regression ◽  
Unit Increase ◽  
Impaired Health ◽  
Simple Logistic

Depression is associated with impaired health outcomes. This study investigated whether there is a significant association between depression and dissatisfaction with dentures in older adults. In a population-based study (1180 adults aged 65–74 yrs), depression was measured by an abbreviated Geriatric Depression Scale. Denture dissatisfaction was assessed with a five-point Likert-type question ("very dissatisfied" to "very satisfied"). The depression-denture dissatisfaction association was analyzed with simple (dissatisfied vs. not dissatisfied outcome) and ordinal logistic regression (based on outcome’s full range). For each unit increase on the 15-point depression scale, the probability of denture dissatisfaction increased by 24% [95% confidence interval, 15–34%, P < 0.001 (simple logistic regression)] and the probability for higher levels on the five-point dissatisfaction scale increased by 16% [95% CI, 11–22%, P < 0.001 (ordinal logistic regression)], adjusted for potential confounding variables. The likely causal association in older adults has major implications for the evaluation of treatment effects and the demand for prosthodontic therapy.

Abstract 16967: Increased Blood Pressure Variability is Associated With Worse Outcome in Acute Ischemic Stroke

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Adam de Havenon ◽  
Haimei Wang ◽  
Greg Stoddard ◽  
Lee Chung ◽  
Jennifer Majersik
Keyword(s):  
Blood Pressure ◽  
Logistic Regression ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Blood Pressure Variability ◽  
Harmful Effect ◽  
Final Analysis ◽  
Ordinal Logistic Regression ◽  
Stroke Severity ◽  
The Mean

Background: Increased blood pressure variability (BPV) is detrimental in the weeks to months after ischemic stroke, but it has not been adequately studied in the acute phase. We hypothesized that increased BPV in acute ischemic stroke (AIS) patients would be associated with worse outcome. Methods: We retrospectively reviewed inpatients at our hospital between 2010-2014 with an ICD-9 code of AIS; 213 were confirmed to have AIS by a vascular neurologist. A modified Rankin Score (mRS) after discharge was available in 148/213, at a mean of 86 ± 60 days. In 45/213 the discharge mRS was either 0 or 6, in which case they were included in the final analysis. BPV was measured as the standard deviation (SD) of each patient’s systolic blood pressure readings during the first 24 hours and 5 days of hospitalization (9,844 total readings), or until discharge if discharged in <5 days (Figure 1). The SBP SD was further divided in quartiles. A multivariate ordinal logistic regression with the outcome of mRS, the primary predictor of quartiles of SBP SD, and baseline NIH stroke scale (NIHSS) to control for initial stroke severity. Results: Mean±SD age was 64.2 ± 16.3 years, NIHSS was 12.6 ± 7.9, and mRS was 2.7 ± 2.1. The mean SBP SDs for the first 24 hours and 5 days were 12.1 ± 6.2 mm Hg and 14.1 ± 4.9 mm Hg. In the ordinal logistic regression model, the quartiles of SBP SD for the first 24 hours and 5 days were positively associated with higher mRS (OR = 1.37, 95% CI 1.01 - 1.74, p = 0.009; OR = 1.30, 95% CI 1.03 - 1.63, p = 0.028). This effect became even more pronounced in patients with the highest quartile of variability (OR = 2.76, 95% CI 1.29 - 5.88, p = 0.009; OR = 2.10, 95% CI 1.01 - 4.36, p = 0.046). Conclusion: In our cohort of 193 patients with AIS, there was a significant association between increased systolic BPV and worse functional outcome, after controlling for initial stroke severity. This data suggests that increased BPV may have a harmful effect for AIS patients, which warrants a prospective observational study.

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