scholarly journals Investigating The Temporal Trends And Risks Imposed By Different Driver Groups On Other Drivers

2021 ◽  
Author(s):  
Thomas Thanuvelil Philip
Keyword(s):  
Logistic Regression ◽  
Temporal Trends ◽  
Decision Makers ◽  
The Other ◽  
Visible Injury ◽  
Vehicle Collisions ◽  
Logistic Regression Models ◽  
Accident Data ◽  
Precautionary Measures ◽  
Injury Severities

Multi-vehicle traffic collisions usually result in increased injury severities to the more vulnerable drivers involved in those accidents. This research study aims at investigating the temporal trends and risks imposed by different driver groups on other drivers using logistic regression. The study is based on analysing accident data for all light-duty two-vehicle collisions in North Carolina from January 1, 2004 to December 31, 2013. Two logistic regression models are developed for each year. The first model, evaluates the probability that a certain driver sustains at least a visible injury caused by the other driver and the second model, evaluates the probability that a driver will cause at least a visible injury to the other driver. The findings of this research may help decision makers identify driver groups that are more dangerous to other drivers so that necessary precautionary measures can be adopted to make our roads a safer place.

scholarly journals Investigating The Temporal Trends And Risks Imposed By Different Driver Groups On Other Drivers

2021 ◽  
Author(s):  
Thomas Thanuvelil Philip
Keyword(s):  
Logistic Regression ◽  
Temporal Trends ◽  
Decision Makers ◽  
The Other ◽  
Visible Injury ◽  
Vehicle Collisions ◽  
Logistic Regression Models ◽  
Accident Data ◽  
Precautionary Measures ◽  
Injury Severities

Multi-vehicle traffic collisions usually result in increased injury severities to the more vulnerable drivers involved in those accidents. This research study aims at investigating the temporal trends and risks imposed by different driver groups on other drivers using logistic regression. The study is based on analysing accident data for all light-duty two-vehicle collisions in North Carolina from January 1, 2004 to December 31, 2013. Two logistic regression models are developed for each year. The first model, evaluates the probability that a certain driver sustains at least a visible injury caused by the other driver and the second model, evaluates the probability that a driver will cause at least a visible injury to the other driver. The findings of this research may help decision makers identify driver groups that are more dangerous to other drivers so that necessary precautionary measures can be adopted to make our roads a safer place.

Domestic Violence Reported to the Police: Correlates of Victims’ Reporting Behavior and Support to Legal Proceedings

2016 ◽  
Vol 31 (3) ◽  
pp. 402-415 ◽  
Author(s):  
Rémi Boivin ◽  
Chloé Leclerc
Keyword(s):  
Domestic Violence ◽  
Logistic Regression ◽  
Regression Models ◽  
Multinomial Logistic Regression ◽  
The Other ◽  
Individual Factors ◽  
Initial Report ◽  
Logistic Regression Models ◽  
Legal Proceedings ◽  
History Of

This article analyzes reported incidents of domestic violence according to the source of the complaint and whether the victim initially supported judicial action against the offender. Almost three quarters of incidents studied were reported by the victim (72%), and a little more than half of victims initially wanted to press charges (55%). Using multinomial logistic regression models, situational and individual factors are used to distinguish 4 incident profiles. Incidents in which the victim made the initial report to the police and wished to press charges are the most distinct and involve partners who were already separated at the time of the incident or had a history of domestic violence. The other profiles also show important differences.

Increasing early ambulation disability in spontaneous intracerebral hemorrhage survivors

Neurology ◽  
2018 ◽  
Vol 90 (23) ◽  
pp. e2017-e2024 ◽  
Author(s):  
Yannick Béjot ◽  
Christelle Blanc ◽  
Benoit Delpont ◽  
Pierre Thouant ◽  
Cécile Chazalon ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Intracerebral Hemorrhage ◽  
Acute Care ◽  
Temporal Trends ◽  
Population Based ◽  
Spontaneous Intracerebral Hemorrhage ◽  
Early Ambulation ◽  
Logistic Regression Models ◽  
Population Based Registry ◽  
Care Ward

ObjectiveTo evaluate temporal trends in early ambulatory status in patients with spontaneous intracerebral hemorrhage (ICH).MethodsAll patients with ICH between 1985 and 2011 were prospectively registered in a population-based registry in Dijon, France, and included in the study. Outcomes of ICH survivors were assessed at discharge from their stay in an acute care ward with the use of a 4-grade ambulation scale. Time trends in ambulation disability and place of discharge were analyzed in 3 periods (1985–1993, 1994–2002, and 2003–2011). Multivariable ordinal and logistic regression models were applied.ResultsFive hundred thirty-one patients with ICH were registered, of whom 200 (37.7%) died in the acute care ward. While the proportion of deaths decreased over time, that of patients with ambulation disability increased (odds ratio [OR] 1.67, 95% confidence interval [CI] 0.87–3.23, p = 0.124 for 1994–2002; and OR 1.97, 95% CI, 1.08–3.60, p = 0.027 for 2003–2011 vs 1985–1993 in ordinal logistic regression). The proportion of patients dependent in walking rose (OR 2.11, 95% CI 1.16–3.82, p = 0.014 for 1994–2002; and OR 2.73; 95% CI 1.54–4.84, p = 0.001 for 2003–2011), and the proportion of patients discharged to home decreased (OR 0.49, 95% CI 0.24–0.99, p = 0.048 for 1994–2002; and OR 0.32, 95% CI 0.16–0.64, p = 0.001 for 2003–2011).ConclusionThe decrease in in-hospital mortality of patients with ICH translated into a rising proportion of patients with ambulation disability at discharge. A lower proportion of patients returned home. These results have major implications for the organization of postacute ICH care.

scholarly journals Transitions from and to roll-your-own tobacco, perceptions and health beliefs among smokers: findings from the EUREST-PLUS ITC Europe Surveys

2020 ◽  
Vol 30 (Supplement_3) ◽  
pp. iii18-iii25
Author(s):  
Filippos T Filippidis ◽  
Pete Driezen ◽  
Christina N Kyriakos ◽  
Paraskevi Katsaounou ◽  
Ioanna Petroulia ◽  
...  
Keyword(s):  
Logistic Regression ◽  
European Country ◽  
Health Effects ◽  
Health Beliefs ◽  
Regression Models ◽  
Lower Cost ◽  
The Other ◽  
Logistic Regression Models ◽  
Low Education ◽  
Sociodemographic Correlates

Abstract Background The prevalence of roll-your-own tobacco (RYO) in Europe has been increasing. The aim of this study was to investigate transitions between factory-made (FM) cigarettes and RYO in a longitudinal sample of European smokers, and their perceptions of relative harmfulness and knowledge of health effects. Methods We used data collected from the EUREST-PLUS ITC 6 European Country (6E) Surveys in 2016 (n = 6011 smokers) and in 2018 (n = 6027) in Germany, Greece, Hungary, Poland, Romania and Spain. A total of 3195 cohort respondents were interviewed in both years. Use of RYO and FM, knowledge of health effects of smoking as well as perceptions about RYO were assessed. We used logistic regression models to explore sociodemographic correlates of transitions from one product to the other, of perceptions and knowledge related to smoking health effects. Results Approximately 7.4% of exclusive FM smokers transitioned to RYO and 29.5% of exclusive RYO smokers transitioned to FM cigarettes from 2016 to 2018. RYO use in 2018 was more frequent among smokers of low education and income, but none of these factors were associated with transitions. Most RYO smokers perceived RYO as cheaper than FM and 21.7% of them considered RYO to be less harmful than FM. Knowledge of the health effects of smoking was not associated with type of product smoked. Conclusions RYO is popular among European smokers; its lower cost seems to be a major factor for RYO users; reasons for transitions to and from RYO are less clear and need to be further investigated.

scholarly journals Trends and Social Barriers for Inpatient Palliative Care in Patients With Metastatic Bladder Cancer Receiving Critical Care Therapies

2019 ◽  
Vol 17 (11) ◽  
pp. 1344-1352 ◽  
Author(s):  
Elio Mazzone ◽  
Sophie Knipper ◽  
Francesco A. Mistretta ◽  
Carlotta Palumbo ◽  
Zhe Tian ◽  
...  
Keyword(s):  
Palliative Care ◽  
Logistic Regression ◽  
Critical Care ◽  
Liver Metastases ◽  
Regression Models ◽  
Temporal Trends ◽  
Recent Analysis ◽  
Logistic Regression Models ◽  
Multivariable Logistic Regression ◽  
Nationwide Inpatient Sample Database

Background: Use of inpatient palliative care (IPC) in the treatment of advanced cancer represents a well-established guideline recommendation. A recent analysis showed that patients with genitourinary cancer benefit from IPC at the second lowest rate among 4 examined primary cancers, namely lung, breast, colorectal, and genitourinary. Based on this observation, temporal trends and predictors of IPC use were examined in patients with metastatic urothelial carcinoma of the bladder (mUCB) receiving critical care therapies (CCTs). Patients and Methods: Patients with mUCB receiving CCTs were identified within the Nationwide Inpatient Sample database (2004–2015). IPC use rates were evaluated in estimated annual percentage change (EAPC) analyses. Multivariable logistic regression models with adjustment for clustering at the hospital level were used. Results: Of 1,944 patients with mUCB receiving CCTs, 191 (9.8%) received IPC. From 2004 through 2015, IPC use increased from 0.7% to 25.0%, respectively (EAPC, +23.9%; P<.001). In analyses stratified according to regions, the highest increase in IPC use was recorded in the Northeast (EAPC, +44.0%), followed by the West (EAPC, +26.8%), South (EAPC, +22.9%), and Midwest (EAPC, +15.5%). Moreover, the lowest rate of IPC adoption in 2015 was recorded in the Midwest (14.3%). In multivariable logistic regression models, teaching status (odds ratio [OR], 1.97; P<.001), more recent diagnosis (2010–2015; OR, 3.89; P<.001), and presence of liver metastases (OR, 1.77; P=.02) were associated with higher IPC rates. Conversely, Hispanic race (OR, 0.42; P=.03) and being hospitalized in the Northeast (OR, 0.36; P=.01) were associated with lower rate of IPC adoption. Finally, patients with a primary admission diagnosis that consisted of infection (OR, 2.05; P=.002), cardiovascular disorders (OR, 2.10; P=.03), or pulmonary disorders (OR, 2.81; P=.005) were more likely to receive IPC. Conclusions: The rate of IPC use in patients with mUCB receiving CCTs sharply increased between 2004 and 2015. The presence of liver metastases, infections, or cardiopulmonary disorders as admission diagnoses represented independent predictors of higher IPC use. Conversely, Hispanic race, nonteaching hospital status, and hospitalization in the Midwest were identified as independent predictors of lower IPC use and represent targets for efforts to improve IPC delivery in patients with mUCB receiving CCT.

Predicting Severity of Accidents in Malaysia By Ordinal Logistic Regression Models

2021 ◽  
Vol 03 (01) ◽  
pp. 11-16
Author(s):  
Uneb Gazder ◽  
Ashar Ahmed ◽  
Umaira Shahid
Keyword(s):  
Land Use ◽  
Logistic Regression ◽  
Road Safety ◽  
Regression Models ◽  
Ordinal Logistic Regression ◽  
Logistic Regression Models ◽  
Accident Severity ◽  
Accident Data ◽  
Road Width ◽  
Research Institute

This study was aimed at determining the relationships of accident severity using road environment and traveller characteristics. Ordinal logistic regression models were used in this study. The accident data was provided by Malaysian Research Institute of Road Safety (MIROS) for all accidents which occurred in Penang state during 2006-2011. It was observed that motorbikes were predominantly involved in these accidents, hence, it was decided to develop three separate models; one for the overall data, and others for accidents with and without motorbikes. Logistic regression models showed that commercial land use, road width and experience of driver are important factors that may increase severity of accidents. Shoulder width was found to decrease the severity of motorbike accidents. Commercial land use, road width and driver experience have more impact on motorbike accidents as compared to accidents of other vehicles.

scholarly journals Models of Forecasting in Financial Analysis of Non-Financial Corporations

2018 ◽  
Author(s):  
Eva Litavcová ◽  
Sylvia Jenčová ◽  
Róbert Štefko
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Financial Analysis ◽  
Slovak Republic ◽  
Regression Method ◽  
Decision Makers ◽  
Logistic Regression Models ◽  
Financial Metrics ◽  
Logistic Regression Method ◽  
Probability Of Bankruptcy

Corporate Diagnosis is now recognized as an important tool by decision makers to predict and correct burgeoning problems that a corporation may face. Methods based on this model stem from the use of mathematics and are increasingly being applied in the analysis of production processes. The goal of this paper is to use a logistic regression to design a scoring model for non-financial corporations in industry. Based on the data obtained from the Registry of the Slovak Republic for 738 non-financial corporations, according to SK NACE 26, SK NACE 27, the proportional financial metrics, using the logistic regression method, were calculated. By applying these methods, two logistic regression models were found to reliably estimate the probability of bankruptcy for a firm.

The impact of SPY angiography on intraoperative decision making and outcomes for post-mastectomy reconstruction

2019 ◽  
pp. 1-5
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Skin Necrosis ◽  
Regression Models ◽  
Multivariable Analysis ◽  
P Value ◽  
Flap Necrosis ◽  
Single Institution ◽  
Logistic Regression Models ◽  
The Impact

Objective: While the use of intraoperative laser angiography (SPY) is increasing in mastectomy patients, its impact in the operating room to change the type of reconstruction performed has not been well described. The purpose of this study is to investigate whether SPY angiography influences post-mastectomy reconstruction decisions and outcomes. Methods and materials: A retrospective analysis of mastectomy patients with reconstruction at a single institution was performed from 2015-2017.All patients underwent intraoperative SPY after mastectomy but prior to reconstruction. SPY results were defined as ‘good’, ‘questionable’, ‘bad’, or ‘had skin excised’. Complications within 60 days of surgery were compared between those whose SPY results did not change the type of reconstruction done versus those who did. Preoperative and intraoperative variables were entered into multivariable logistic regression models if significant at the univariate level. A p-value <0.05 was considered significant. Results: 267 mastectomies were identified, 42 underwent a change in the type of planned reconstruction due to intraoperative SPY results. Of the 42 breasts that underwent a change in reconstruction, 6 had a ‘good’ SPY result, 10 ‘questionable’, 25 ‘bad’, and 2 ‘had areas excised’ (p<0.01). After multivariable analysis, predictors of skin necrosis included patients with ‘questionable’ SPY results (p<0.01, OR: 8.1, 95%CI: 2.06 – 32.2) and smokers (p<0.01, OR:5.7, 95%CI: 1.5 – 21.2). Predictors of any complication included a change in reconstruction (p<0.05, OR:4.5, 95%CI: 1.4-14.9) and ‘questionable’ SPY result (p<0.01, OR: 4.4, 95%CI: 1.6-14.9). Conclusion: SPY angiography results strongly influence intraoperative surgical decisions regarding the type of reconstruction performed. Patients most at risk for flap necrosis and complication post-mastectomy are those with questionable SPY results.

Utilizing Twitter Data Analysis and Deep Learning to Identify Drug Use (Preprint)

2019 ◽  
Author(s):  
Joseph Tassone ◽  
Peizhi Yan ◽  
Mackenzie Simpson ◽  
Chetan Mendhe ◽  
Vijay Mago ◽  
...  
Keyword(s):  
Social Media ◽  
Logistic Regression ◽  
Deep Learning ◽  
Decision Tree ◽  
Semantic Meaning ◽  
Predictive Capability ◽  
Logistic Regression Models ◽  
Twitter Data ◽  
Data Points ◽  
Positive Classification

BACKGROUND The collection and examination of social media has become a useful mechanism for studying the mental activity and behavior tendencies of users. OBJECTIVE Through the analysis of a collected set of Twitter data, a model will be developed for predicting positively referenced, drug-related tweets. From this, trends and correlations can be determined. METHODS Twitter social media tweets and attribute data were collected and processed using topic pertaining keywords, such as drug slang and use-conditions (methods of drug consumption). Potential candidates were preprocessed resulting in a dataset 3,696,150 rows. The predictive classification power of multiple methods was compared including regression, decision trees, and CNN-based classifiers. For the latter, a deep learning approach was implemented to screen and analyze the semantic meaning of the tweets. RESULTS The logistic regression and decision tree models utilized 12,142 data points for training and 1041 data points for testing. The results calculated from the logistic regression models respectively displayed an accuracy of 54.56% and 57.44%, and an AUC of 0.58. While an improvement, the decision tree concluded with an accuracy of 63.40% and an AUC of 0.68. All these values implied a low predictive capability with little to no discrimination. Conversely, the CNN-based classifiers presented a heavy improvement, between the two models tested. The first was trained with 2,661 manually labeled samples, while the other included synthetically generated tweets culminating in 12,142 samples. The accuracy scores were 76.35% and 82.31%, with an AUC of 0.90 and 0.91. Using association rule mining in conjunction with the CNN-based classifier showed a high likelihood for keywords such as “smoke”, “cocaine”, and “marijuana” triggering a drug-positive classification. CONCLUSIONS Predictive analysis without a CNN is limited and possibly fruitless. Attribute-based models presented little predictive capability and were not suitable for analyzing this type of data. The semantic meaning of the tweets needed to be utilized, giving the CNN-based classifier an advantage over other solutions. Additionally, commonly mentioned drugs had a level of correspondence with frequently used illicit substances, proving the practical usefulness of this system. Lastly, the synthetically generated set provided increased scores, improving the predictive capability. CLINICALTRIAL None

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