scholarly journals Crash Risk Prediction Model of Lane-Change Behavior on Approaching Intersections

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Yingshuai Li ◽  
Jian Lu ◽  
Kuisheng Xu
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Risk Index ◽  
Video Recording ◽  
Risk Prediction Model ◽  
Risk Level ◽  
Lane Change ◽  
Trajectory Data ◽  
Lane Changing ◽  
Conflict Risk

The driving tendency of drivers is one of the most important factors in lane-changing maneuvers. However, the heterogeneity of the characteristics of drivers’ lane-changing behaviors has not been adequately considered. The primary objective of the present study is to explore the risk level of the lane-changing implementation process under different driving tendencies upon approaching signalized intersections in an urban area. This paper defines the Integrated Conflict Risk Index (ICRI), which takes into account the probability and severity of risk. Using the index as the dependent variable, the risk prediction model of implementing the lane-change process is established. A series of experiments, which included a questionnaire, a number of tests, and on-road experiments, was conducted to identify the driving tendencies of the participants. A combination of video recording and instrumented vehicles was used to collect lane-changing trajectory data of different driving tendencies. The parameters of the model were calibrated, and the results indicate that driving tendency has a significant effect on the risk level of lane-changing execution. More specifically, the more aggressive the driving tendency, the higher the risk level. The quantitative results of the study can provide the basis for conflict risk assessment in the existing lane-changing models.

Development of an Oncology Acute Care Risk Prediction Model

2021 ◽  
pp. 266-271
Author(s):  
Valerie P. Csik ◽  
Michael Li ◽  
Adam F. Binder ◽  
Nathan R. Handley
Keyword(s):  
High Risk ◽  
Prediction Model ◽  
Acute Care ◽  
Risk Prediction ◽  
Risk Index ◽  
Risk Prediction Model ◽  
Discrete Point ◽  
Allocation System ◽  
Ed Visits ◽  
Active Cancer

PURPOSE Acute care utilization (ACU), including emergency department (ED) visits or hospital admissions, is common in patients with cancer and may be preventable. The Center for Medicare & Medicaid Services recently implemented OP-35, a measure in the Hospital Outpatient Quality Reporting Program focused on ED visits and inpatient admissions for 10 potentially preventable conditions that arise within 30 days of chemotherapy. This new measure exemplifies a growing focus on preventing unnecessary ACU. However, identifying patients at high risk of ACU remains a challenge. We developed a real-time clinical prediction model using a discrete point allocation system to assess risk for ACU in patients with active cancer. METHODS We performed a retrospective cohort analysis of patients with active cancer from a large urban academic medical center. The primary outcome, ACU, was evaluated using a multivariate logistic regression model with backward variable selection. We used estimates from the multivariate logistic model to construct a risk index using a discrete point allocation system. RESULTS Eight thousand two hundred forty-six patients were included in the analysis. ED utilization in the last 90 days, history of chronic obstructive pulmonary disease, congestive heart failure or renal failure, and low hemoglobin and low neutrophil count significantly increased risk for ACU. The model produced an overall C-statistic of 0.726. Patients defined as high risk (achieving a score of 2 or higher on the risk index) represented 10% of total patients and 46% of ACU. CONCLUSION We developed an oncology acute care risk prediction model using a risk index–based scoring system, the REDUCE (Reducing ED Utilization in the Cancer Experience) score. Further efforts to evaluate the effectiveness of our model in predicting ACU are ongoing.

scholarly journals A Model for Risk Prediction of Cerebrovascular Disease Prevalence—Based on Community Residents Aged 40 and above in a City in China

2021 ◽  
Vol 18 (12) ◽  
pp. 6584
Author(s):  
Qin Zhu ◽  
Die Luo ◽  
Xiaojun Zhou ◽  
Xianxu Cai ◽  
Qi Li ◽  
...  
Keyword(s):  
Risk Factors ◽  
Prediction Model ◽  
Cerebrovascular Disease ◽  
Risk Prediction ◽  
Disease Risk ◽  
Risk Index ◽  
Risk Prediction Model ◽  
Jiangxi Province ◽  
Community Residents ◽  
Local Residents

Cerebrovascular disease (CVD) is the leading cause of death in many countries including China. Early diagnosis and risk assessment represent one of effective approaches to reduce the CVD-related mortality. The purpose of this study was to understand the prevalence and influencing factors of cerebrovascular disease among community residents in Qingyunpu District, Nanchang City, Jiangxi Province, and to construct a model of cerebrovascular disease risk index suitable for local community residents. A stratified cluster sampling method was used to sample 2147 community residents aged 40 and above, and the prevalence of cerebrovascular diseases and possible risk factors were investigated. It was found that the prevalence of cerebrovascular disease among local residents was 4.5%. Poisson regression analysis found that old age, lack of exercise, hypertension, diabetes, smoking, and family history of cerebrovascular disease are the main risk factors for local cerebrovascular disease. The relative risk ORs were 3.284, 2.306, 2.510, 3.194, 1.949, 2.315, respectively. For these six selected risk factors, a cerebrovascular disease risk prediction model was established using the Harvard Cancer Index method. The R value of the risk prediction model was 1.80 (sensitivity 81.8%, specificity 47.0%), which was able to well predict the risk of cerebrovascular disease among local residents. This provides a scientific basis for the further development of local cerebrovascular disease prevention and control work.

Development of Neurobehavioral Deterioration Risk Prediction Model for Welder: A Proposed Study

2018 ◽  
Vol 10 (5) ◽  
Author(s):  
Nuur Azreen Paiman ◽  
◽  
Azian Hariri ◽  
Ibrahim Masood ◽  
Arma Noor ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model

Wound dehiscence after radical cystectomy: A novel risk-prediction model

2021 ◽  
Vol 79 ◽  
pp. S1112-S1113
Author(s):  
A.A. Nasrallah ◽  
M. Mansour ◽  
C.H. Ayoub ◽  
N. Abou Heidar ◽  
J.A. Najdi ◽  
...  
Keyword(s):  
Prediction Model ◽  
Radical Cystectomy ◽  
Risk Prediction ◽  
Wound Dehiscence ◽  
Risk Prediction Model

scholarly journals Protocol for the development and validation of a risk prediction model for stillbirths from 35 weeks gestation in Australia

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Jessica K. Sexton ◽  
Michael Coory ◽  
Sailesh Kumar ◽  
Gordon Smith ◽  
Adrienne Gordon ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Prediction Model ◽  
Risk Prediction ◽  
Late Pregnancy ◽  
Risk Prediction Model ◽  
Past Century ◽  
Reporting Standards ◽  
Robust Method ◽  
Predictive Values ◽  
Multivariable Logistic Regression

Abstract Background Despite advances in the care of women and their babies in the past century, an estimated 1.7 million babies are born still each year throughout the world. A robust method to estimate a pregnant woman’s individualized risk of late-pregnancy stillbirth is needed to inform decision-making around the timing of birth to reduce the risk of stillbirth from 35 weeks of gestation in Australia, a high-resource setting. Methods This is a protocol for a cross-sectional study of all late-pregnancy births in Australia (2005–2015) from 35 weeks of gestation including 5188 stillbirths among 3.1 million births at an estimated rate of 1.7 stillbirths per 1000 births. A multivariable logistic regression model will be developed in line with current TransparentReporting of a multivariable prediction model forIndividualPrognosis orDiagnosis (TRIPOD) guidelines to estimate the gestation-specific probability of stillbirth with prediction intervals. Candidate predictors were identified from systematic reviews and clinical consultation and will be described through univariable regression analysis. To generate a final model, elimination by backward stepwise multivariable logistic regression will be performed. The model will be internally validated using bootstrapping with 1000 repetitions and externally validated using a temporally unique dataset. Overall model performance will be assessed with R2, calibration, and discrimination. Calibration will be reported using a calibration plot with 95% confidence intervals (α = 0.05). Discrimination will be measured by the C-statistic and area underneath the receiver-operator curves. Clinical usefulness will be reported as positive and negative predictive values, and a decision curve analysis will be considered. Discussion A robust method to predict a pregnant woman’s individualized risk of late-pregnancy stillbirth is needed to inform timely, appropriate care to reduce stillbirth. Among existing prediction models designed for obstetric use, few have been subject to internal and external validation and many fail to meet recommended reporting standards. In developing a risk prediction model for late-gestation stillbirth with both providers and pregnant women in mind, we endeavor to develop a validated model for clinical use in Australia that meets current reporting standards.

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