Prediction of Traffic Accident Severity Based on Random Forest and Logistic Regression Model

2019 ◽  
Vol 09 (10) ◽  
pp. 1920-1927
Author(s):  
小刚 郭
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Regression Model ◽  
Traffic Accident ◽  
Logistic Regression Model ◽  
Accident Severity

scholarly journals Effect of fluid administration on scene to traffic accident patients by EMS personnel: a propensity score-matched study using population-based ambulance records and nationwide trauma registry in Japan

2021 ◽  
Author(s):  
Yusuke Katayama ◽  
Tetsuhisa Kitamura ◽  
Kosuke Kiyohara ◽  
Kenichiro Ishida ◽  
Tomoya Hirose ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Propensity Score ◽  
Hospital Mortality ◽  
Regression Model ◽  
Traffic Accident ◽  
Logistic Regression Model ◽  
Cardiopulmonary Arrest ◽  
Population Based ◽  
Fluid Administration ◽  
Secondary Outcome

Abstract Purpose The aim of this study was to assess the effect of fluid administration by emergency life-saving technicians (ELST) on the prognosis of traffic accident patients by using a propensity score (PS)-matching method. Methods The study included traffic accident patients registered in the JTDB database from January 2016 to December 2017. The main outcome was hospital mortality, and the secondary outcome was cardiopulmonary arrest on hospital arrival (CPAOA). To reduce potential confounding effects in the comparisons between two groups, we estimated a propensity score (PS) by fitting a logistic regression model that was adjusted for 17 variables before the implementation of fluid administration by ELST at the scene. Results During the study period, 10,908 traffic accident patients were registered in the JTDB database, and we included 3502 patients in this study. Of these patients, 142 were administered fluid by ELST and 3360 were not administered fluid by ELST. After PS matching, 141 patients were selected from each group. In the PS-matched model, fluid administration by ELST at the scene was not associated with discharge to death (crude OR: 0.859 [95% CI, 0.500–1.475]; p = 0.582). However, the fluid group showed statistically better outcome for CPAOA than the no fluid group in the multiple logistic regression model (adjusted OR: 0.231 [95% CI, 0.055–0.967]; p = 0.045). Conclusion In this study, fluid administration to traffic accident patients by ELST was associated not with hospital mortality but with a lower proportion of CPAOA.

scholarly journals Machine-Learning vs. Expert-Opinion Driven Logistic Regression Modelling for Predicting 30-Day Unplanned Rehospitalisation in Preterm Babies: A Prospective, Population-Based Study (EPIPAGE 2)

2021 ◽  
Vol 8 ◽  
Author(s):  
Robert A. Reed ◽  
Andrei S. Morgan ◽  
Jennifer Zeitlin ◽  
Pierre-Henri Jarreau ◽  
Héloïse Torchin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Regression Model ◽  
Expert Opinion ◽  
Logistic Regression Model ◽  
Population Based ◽  
Regression Modelling ◽  
Preterm Babies ◽  
Logistic Regression Modelling

Introduction: Preterm babies are a vulnerable population that experience significant short and long-term morbidity. Rehospitalisations constitute an important, potentially modifiable adverse event in this population. Improving the ability of clinicians to identify those patients at the greatest risk of rehospitalisation has the potential to improve outcomes and reduce costs. Machine-learning algorithms can provide potentially advantageous methods of prediction compared to conventional approaches like logistic regression.Objective: To compare two machine-learning methods (least absolute shrinkage and selection operator (LASSO) and random forest) to expert-opinion driven logistic regression modelling for predicting unplanned rehospitalisation within 30 days in a large French cohort of preterm babies.Design, Setting and Participants: This study used data derived exclusively from the population-based prospective cohort study of French preterm babies, EPIPAGE 2. Only those babies discharged home alive and whose parents completed the 1-year survey were eligible for inclusion in our study. All predictive models used a binary outcome, denoting a baby's status for an unplanned rehospitalisation within 30 days of discharge. Predictors included those quantifying clinical, treatment, maternal and socio-demographic factors. The predictive abilities of models constructed using LASSO and random forest algorithms were compared with a traditional logistic regression model. The logistic regression model comprised 10 predictors, selected by expert clinicians, while the LASSO and random forest included 75 predictors. Performance measures were derived using 10-fold cross-validation. Performance was quantified using area under the receiver operator characteristic curve, sensitivity, specificity, Tjur's coefficient of determination and calibration measures.Results: The rate of 30-day unplanned rehospitalisation in the eligible population used to construct the models was 9.1% (95% CI 8.2–10.1) (350/3,841). The random forest model demonstrated both an improved AUROC (0.65; 95% CI 0.59–0.7; p = 0.03) and specificity vs. logistic regression (AUROC 0.57; 95% CI 0.51–0.62, p = 0.04). The LASSO performed similarly (AUROC 0.59; 95% CI 0.53–0.65; p = 0.68) to logistic regression.Conclusions: Compared to an expert-specified logistic regression model, random forest offered improved prediction of 30-day unplanned rehospitalisation in preterm babies. However, all models offered relatively low levels of predictive ability, regardless of modelling method.

Relevance of Brain Regions’ Eloquence Assessment in Patients With a Large Ischemic Core Treated With Mechanical Thrombectomy

Neurology ◽  
2021 ◽  
pp. 10.1212/WNL.0000000000012863
Author(s):  
Basile Kerleroux ◽  
Joseph Benzakoun ◽  
Kévin Janot ◽  
Cyril Dargazanli ◽  
Dimitri Daly Eraya ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Mechanical Thrombectomy ◽  
Predictive Accuracy ◽  
Unmet Need ◽  
C Statistic ◽  
Ischemic Core ◽  
Functional Relevance

ObjectiveIndividualized patient selection for mechanical thrombectomy (MT) in patients with acute ischemic stroke (AIS) and large ischemic core (LIC) at baseline is an unmet need.We tested the hypothesis, that assessing the functional relevance of both the infarcted and hypo-perfused brain tissue, would improve the selection framework of patients with LIC for MT.MethodsMulticenter, retrospective, study of adult with LIC (ischemic core volume > 70ml on MR-DWI), with MRI perfusion, treated with MT or best medical management (BMM).Primary outcome was 3-month modified-Rankin-Scale (mRS), favourable if 0-3. Global and regional-eloquence-based core-perfusion mismatch ratios were derived. The predictive accuracy for clinical outcome of eloquent regions involvement was compared in multivariable and bootstrap-random-forest models.ResultsA total of 138 patients with baseline LIC were included (MT n=96 or BMM n=42; mean age±SD, 72.4±14.4years; 34.1% females; mRS=0-3: 45.1%). Mean core and critically-hypo-perfused volume were 100.4ml±36.3ml and 157.6±56.2ml respectively and did not differ between groups. Models considering the functional relevance of the infarct location showed a better accuracy for the prediction of mRS=0-3 with a c-Statistic of 0.76 and 0.83 for logistic regression model and bootstrap-random-forest testing sets respectively. In these models, the interaction between treatment effect of MT and the mismatch was significant (p=0.04). In comparison in the logistic regression model disregarding functional eloquence the c-Statistic was 0.67 and the interaction between MT and the mismatch was insignificant.ConclusionConsidering functional eloquence of hypo-perfused tissue in patients with a large infarct core at baseline allows for a more precise estimation of treatment expected benefit.

scholarly journals An examination of accident severity differences between male and female drivers, Using Logistic Regression Model

2015 ◽  
Vol 1 (1) ◽  
pp. 31-36
Author(s):  
Alireza Pakgohar ◽  
Mojtaba Kazemi
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Accuracy Rate ◽  
Model Accuracy ◽  
Male And Female ◽  
Accident Severity ◽  
Probability Of Death

One person in every 2539 people gets killed and one in every 253 suffers injuries due to driving crashes each year in Iran. Such that driving incidents are second rank factor of death and the first rank reason for lost lifetimes in this country. 60% of total incidents which lead to deaths or injuries are actually driving incidents in Iran. That is while the same ratio is only 25% worldwide average. In this article, we report a probabilistic relationship between vehicle drivers’ gender and severity of the accidents. The model accuracy rate is more than 91%. Coefficient values show that if an crash happens and all other variables are under control, the probability of suffering injuries for a man is 1.597 times more than for a woman (1.40 – 1.79, 99% CI) in comparison with the case that the person does not get injured at all. Similarly, the probability of death for a man is 1.462 times higher than for a woman (1.13-1.79, 90% CI) again in comparison with case of no injury at all.

scholarly journals Analysis of Traffic Accident Severity at Intersection Using Logistic Regression Model

2020 ◽  
pp. 1-9
Author(s):  
Yao Tzu Hsu ◽  
Shun Chi Chang ◽  
Tzu Hsin Hsu
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Traffic Safety ◽  
Logistic Regression Model ◽  
Surface Condition ◽  
Confusion Matrix ◽  
Weather Condition ◽  
Road Surface ◽  
Vehicle Type ◽  
Accident Severity

Accident severity analysis is an important issue in the field of traffic safety study, and intersections are also locations of relatively high accident rates in the roadway network. Therefore, the main purpose of this study is to establish a prediction model of intersection severity based on the binary logistic regression model of data mining technology. The data source of intersection accident is obtained from the Taichung City Police Department in Taiwan in 2018 and there are 27461 valid samples. The dependent variable is the severity of intersection accident. The independent variables include 9 variables such as month, time of accident, weather condition, light conditions, road type, road surface condition, traffic control type, accident type and vehicle type, and are analyzed by the forward selection (Wald). The research results show that time of accident, road surface condition, accident type and vehicle type have significant effects. The confusion matrix is used to verify the reliability of the model, and the results can be used as the references for reducing the degree of accident injury at the intersection in the future.

scholarly journals The statistical importance of P-POSSUM scores for predicting mortality after emergency laparotomy in geriatric patients

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Yang Cao ◽  
Gary A. Bass ◽  
Rebecka Ahl ◽  
Arvid Pourlotfi ◽  
Håkan Geijer ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Regression Model ◽  
Mortality Risk ◽  
Logistic Regression Model ◽  
Geriatric Patients ◽  
Random Forest Model ◽  
Emergency Laparotomy ◽  
Relative Importance ◽  
Forest Model

Abstract Background Geriatric patients frequently undergo emergency general surgery and accrue a greater risk of postoperative complications and fatal outcomes than the general population. It is highly relevant to develop the most appropriate care measures and to guide patient-centered decision-making around end-of-life care. Portsmouth - Physiological and Operative Severity Score for the enumeration of Mortality and morbidity (P-POSSUM) has been used to predict mortality in patients undergoing different types of surgery. In the present study, we aimed to evaluate the relative importance of the P-POSSUM score for predicting 90-day mortality in the elderly subjected to emergency laparotomy from statistical aspects. Methods One hundred and fifty-seven geriatric patients aged ≥65 years undergoing emergency laparotomy between January 1st, 2015 and December 31st, 2016 were included in the study. Mortality and 27 other patient characteristics were retrieved from the computerized records of Örebro University Hospital in Örebro, Sweden. Two supervised classification machine methods (logistic regression and random forest) were used to predict the 90-day mortality risk. Three scalers (Standard scaler, Robust scaler and Min-Max scaler) were used for variable engineering. The performance of the models was evaluated using accuracy, sensitivity, specificity and area under the receiver operating characteristic curve (AUC). Importance of the predictors were evaluated using permutation variable importance and Gini importance. Results The mean age of the included patients was 75.4 years (standard deviation =7.3 years) and the 90-day mortality rate was 29.3%. The most common indication for surgery was bowel obstruction occurring in 92 (58.6%) patients. Types of post-operative complications ranged between 7.0–36.9% with infection being the most common type. Both the logistic regression and random forest models showed satisfactory performance for predicting 90-day mortality risk in geriatric patients after emergency laparotomy, with AUCs of 0.88 and 0.93, respectively. Both models had an accuracy > 0.8 and a specificity ≥0.9. P-POSSUM had the greatest relative importance for predicting 90-day mortality in the logistic regression model and was the fifth important predictor in the random forest model. No notable change was found in sensitivity analysis using different variable engineering methods with P-POSSUM being among the five most accurate variables for mortality prediction. Conclusion P-POSSUM is important for predicting 90-day mortality after emergency laparotomy in geriatric patients. The logistic regression model and random forest model may have an accuracy of > 0.8 and an AUC around 0.9 for predicting 90-day mortality. Further validation of the variables’ importance and the models’ robustness is needed by use of larger dataset.

scholarly journals Predictive Modelling of Landslide Susceptibility in the Western Carpathian Flysch Zone

Land ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1370
Author(s):  
Mária Barančoková ◽  
Matej Šošovička ◽  
Peter Barančok ◽  
Peter Barančok
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Regression Model ◽  
Landslide Susceptibility ◽  
Logistic Regression Model ◽  
Rank Correlation ◽  
Brier Score ◽  
Northwestern Part ◽  
Conditioning Factors ◽  
Carpathian Flysch

Landslides are the most common geodynamic phenomenon in Slovakia, and the most affected area is the northwestern part of the Kysuca River Basin, in the Western Carpathian flysch zone. In this paper, we evaluate the susceptibility of this region to landslides using logistic regression and random forest models. We selected 15 landslide conditioning factors as potential predictors of a dependent variable (landslide susceptibility). Classes of factors with too detailed divisions were reclassified into more general classes based on similarities of their characteristics. Association between the conditioning factors was measured by Cramer’s V and Spearman’s rank correlation coefficients. Models were trained on two types of datasets—balanced and stratified, and both their classification performance and probability calibration were evaluated using, among others, area under ROC curve (AUC), accuracy (Acc), and Brier score (BS) using 5-fold cross-validation. The random forest model outperformed the logistic regression model in all considered measures and achieved very good results on validation datasets with average values of AUCval=0.967, Accval=0.928, and BSval=0.079. The logistic regression model results also indicate the importance of assessing the calibration of predicted probabilities in landslide susceptibility modelling.

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