The Experts below are selected from a list of 23283 Experts worldwide ranked by ideXlab platform
Stephen Greaves - One of the best experts on this subject based on the ideXlab platform.
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driver behaviour profiles for road safety analysis
Accident Analysis & Prevention, 2015Co-Authors: Adrian B Ellison, Stephen Greaves, Michiel C J BliemerAbstract:Driver behaviour is a contributing factor in over 90 percent of road crashes. As a consequence, there is significant benefit in identifying drivers who engage in unsafe driving practices. Driver behaviour profiles (DBPs) are introduced here as an approach for evaluating driver behaviour as a function of the Risk of a casualty crash. They employ data collected using global positioning system (GPS) devices, supplemented with spatiotemporal information. These profiles are comprised of common Risk scores that can be used to compare drivers between each other and across time and space. The paper details the development of these DBPs and demonstrates their use as an input into modelling the factors that influence driver behaviour. The results show that even having controlled for the influence of the road environment, these factors remain the strongest predictors of driver behaviour suggesting different spatiotemporal environments elicit a variety of psychological responses in drivers. The approach and outcomes will be of interest to insurance companies in enhancing the Risk-Profiling of drivers with on-road driving and government through assessing the impacts of behaviour-change interventions. Language: en
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evaluating changes in driver behaviour a Risk Profiling approach
Accident Analysis & Prevention, 2015Co-Authors: Adrian B Ellison, Michiel C J Bliemer, Stephen GreavesAbstract:New road safety strategies continue to be devised by researchers and policy makers with pay-as-you-drive (PAYD) schemes gaining increasing attention. However, empirically measuring the effectiveness of these strategies is challenging due to the influence of the road environment and other factors external to the driver. The analysis presented here applies Temporal and Spatial Identifiers to control for the road environment and Driver Behaviour Profiles to provide a common measure of driving behaviour based on the Risk of a casualty crash for assessing the effectiveness of a PAYD scheme on reducing driving Risks. The results show that in many cases personalised feedback alone is sufficient to induce significant changes, but the largest reductions in Risk are observed when drivers are also awarded a financial incentive to change behaviour. Importantly, the more frequent the exposure to the speeding information, the greater the magnitude of the change. However, the changes are disproportionately associated with those that were already safer drivers in the baseline period suggesting that some drivers may be predisposed to changing their behaviour. These results suggest that it would be beneficial to provide real-time or daily feedback on speeding behaviour in conjunction with a financial reward scheme, potentially as a component of insurance premiums. Language: en
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evaluating changes in driver behaviour a Risk Profiling approach
Accident Analysis & Prevention, 2015Co-Authors: Adrian B Ellison, Michiel C J Bliemer, Stephen GreavesAbstract:New road safety strategies continue to be devised by researchers and policy makers with pay-as-you-drive (PAYD) schemes gaining increasing attention. However, empirically measuring the effectiveness of these strategies is challenging due to the influence of the road environment and other factors external to the driver. The analysis presented here applies Temporal and Spatial Identifiers to control for the road environment and Driver Behaviour Profiles to provide a common measure of driving behaviour based on the Risk of a casualty crash for assessing the effectiveness of a PAYD scheme on reducing driving Risks. The results show that in many cases personalised feedback alone is sufficient to induce significant changes, but the largest reductions in Risk are observed when drivers are also awarded a financial incentive to change behaviour. Importantly, the more frequent the exposure to the speeding information, the greater the magnitude of the change. However, the changes are disproportionately associated with those that were already safer drivers in the baseline period suggesting that some drivers may be predisposed to changing their behaviour. These results suggest that it would be beneficial to provide real-time or daily feedback on speeding behaviour in conjunction with a financial reward scheme, potentially as a component of insurance premiums.
Maria Timofeeva - One of the best experts on this subject based on the ideXlab platform.
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prediction of colorectal cancer Risk based on Profiling with common genetic variants
International Journal of Cancer, 2020Co-Authors: Maria Timofeeva, Athina Spiliopoulou, Paul M Mckeigue, Xiaomeng Zhang, Victoria Svinti, Harry Campbell, Richard S Houlston, Ian Tomlinson, Susan M FarringtonAbstract:Increasing numbers of common genetic variants associated with colorectal cancer (CRC) have been identified. Our study aimed to determine whether Risk prediction based on common genetic variants might enable stratification for CRC Risk. Meta-analysis of 11 genome-wide association studies comprising 16 871 cases and 26 328 controls was performed to capture CRC susceptibility variants. Genetic prediction models with several candidate polygenic Risk scores (PRSs) were generated from Scottish CRC case-control studies (6478 cases and 11 043 controls) and the score with the best performance was then tested in UK Biobank (UKBB) (4800 cases and 20 287 controls). A weighted PRS of 116 CRC single nucleotide polymorphisms (wPRS116 ) was found with the best predictive performance, reporting a c-statistics of 0.60 and an odds ratio (OR) of 1.46 (95% confidence interval [CI] = 1.41-1.50, per SD increase) in Scottish data set. The predictive performance of this wPRS116 was consistently validated in UKBB data set with c-statistics of 0.61 and an OR of 1.49 (95% CI = 1.44-1.54, per SD increase). Modeling the levels of PRS with age and sex in the general UK population shows that employing genetic Risk Profiling can achieve a moderate degree of Risk discrimination that could be helpful to identify a subpopulation with higher CRC Risk due to genetic susceptibility.
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prediction of colorectal cancer Risk based on Profiling with common genetic variants
medRxiv, 2019Co-Authors: Maria Timofeeva, Athina Spiliopoulou, Paul M Mckeigue, Xiaomeng Zhang, Victoria Svinti, Harry Campbell, Richard S Houlston, Ian Tomlinson, Susan M Farrington, Malcolm G DunlopAbstract:Summary Background Stratifying the Risk of colorectal cancer (CRC) based on polygenic Risk scores (PRSs) within populations has the potential to optimize screening and develop targeted prevention strategies. Methods A meta-analysis of eleven genome-wide association studies (GWAS), comprising 16 871 cases and 26 328 controls, was performed to capture CRC susceptibility variants. Genetic models with several candidate PRSs were generated from Scottish CRC case–control studies (6478 cases and 11 043 controls) for prediction of overall and site-specific CRC. Model performance was validated in UK Biobank (4800 cases and 20 287 controls). The 10-year absolute Risk of CRC was estimated by modelling PRS with age and sex using the CRC incidence and mortality rates in the UK population. Findings A weighted PRS including 116 CRC SNPs (wPRS116) showed the strongest performance. Deconstructing the PRS into multiple genetic Risk regional scores or inclusion of additional SNPs that did not reach genome-wide significance did not provide any further improvement on predictive performance. The odds ratio (OR) for CRC Risk per SD of wPRS116 in Scottish dataset was 1·46 (95%CI: 1·41-1·50, c-statistics: 0·603). Consistent estimates were observed in UK Biobank (OR=1·49, 95%CI: 1·44-1·54, c-statistics: 0·610) and showed no substantial heterogeneity among tumor sites. Compared to the middle quintile, those in the highest 1% of PRSs had 3·25-fold higher Risk and those in the lowest 1% had 0·32-fold lower Risk of developing CRC. Modelling PRS with age and sex in the general UK population allows the identification of a high-Risk group with 10-year absolute Risk ≥5%. Interpretation By optimizing wPRS116, we show that genetic factors increase predictive performance but this increment is equivalent to the extraction of only one-tenth of the genetic susceptibility. When employing genetic Risk Profiling in population settings it provides a degree of Risk discrimination that could, in principle, be integrated into population-based screening programs.
Adrian B Ellison - One of the best experts on this subject based on the ideXlab platform.
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driver behaviour profiles for road safety analysis
Accident Analysis & Prevention, 2015Co-Authors: Adrian B Ellison, Stephen Greaves, Michiel C J BliemerAbstract:Driver behaviour is a contributing factor in over 90 percent of road crashes. As a consequence, there is significant benefit in identifying drivers who engage in unsafe driving practices. Driver behaviour profiles (DBPs) are introduced here as an approach for evaluating driver behaviour as a function of the Risk of a casualty crash. They employ data collected using global positioning system (GPS) devices, supplemented with spatiotemporal information. These profiles are comprised of common Risk scores that can be used to compare drivers between each other and across time and space. The paper details the development of these DBPs and demonstrates their use as an input into modelling the factors that influence driver behaviour. The results show that even having controlled for the influence of the road environment, these factors remain the strongest predictors of driver behaviour suggesting different spatiotemporal environments elicit a variety of psychological responses in drivers. The approach and outcomes will be of interest to insurance companies in enhancing the Risk-Profiling of drivers with on-road driving and government through assessing the impacts of behaviour-change interventions. Language: en
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evaluating changes in driver behaviour a Risk Profiling approach
Accident Analysis & Prevention, 2015Co-Authors: Adrian B Ellison, Michiel C J Bliemer, Stephen GreavesAbstract:New road safety strategies continue to be devised by researchers and policy makers with pay-as-you-drive (PAYD) schemes gaining increasing attention. However, empirically measuring the effectiveness of these strategies is challenging due to the influence of the road environment and other factors external to the driver. The analysis presented here applies Temporal and Spatial Identifiers to control for the road environment and Driver Behaviour Profiles to provide a common measure of driving behaviour based on the Risk of a casualty crash for assessing the effectiveness of a PAYD scheme on reducing driving Risks. The results show that in many cases personalised feedback alone is sufficient to induce significant changes, but the largest reductions in Risk are observed when drivers are also awarded a financial incentive to change behaviour. Importantly, the more frequent the exposure to the speeding information, the greater the magnitude of the change. However, the changes are disproportionately associated with those that were already safer drivers in the baseline period suggesting that some drivers may be predisposed to changing their behaviour. These results suggest that it would be beneficial to provide real-time or daily feedback on speeding behaviour in conjunction with a financial reward scheme, potentially as a component of insurance premiums. Language: en
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evaluating changes in driver behaviour a Risk Profiling approach
Accident Analysis & Prevention, 2015Co-Authors: Adrian B Ellison, Michiel C J Bliemer, Stephen GreavesAbstract:New road safety strategies continue to be devised by researchers and policy makers with pay-as-you-drive (PAYD) schemes gaining increasing attention. However, empirically measuring the effectiveness of these strategies is challenging due to the influence of the road environment and other factors external to the driver. The analysis presented here applies Temporal and Spatial Identifiers to control for the road environment and Driver Behaviour Profiles to provide a common measure of driving behaviour based on the Risk of a casualty crash for assessing the effectiveness of a PAYD scheme on reducing driving Risks. The results show that in many cases personalised feedback alone is sufficient to induce significant changes, but the largest reductions in Risk are observed when drivers are also awarded a financial incentive to change behaviour. Importantly, the more frequent the exposure to the speeding information, the greater the magnitude of the change. However, the changes are disproportionately associated with those that were already safer drivers in the baseline period suggesting that some drivers may be predisposed to changing their behaviour. These results suggest that it would be beneficial to provide real-time or daily feedback on speeding behaviour in conjunction with a financial reward scheme, potentially as a component of insurance premiums.
Michiel C J Bliemer - One of the best experts on this subject based on the ideXlab platform.
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driver behaviour profiles for road safety analysis
Accident Analysis & Prevention, 2015Co-Authors: Adrian B Ellison, Stephen Greaves, Michiel C J BliemerAbstract:Driver behaviour is a contributing factor in over 90 percent of road crashes. As a consequence, there is significant benefit in identifying drivers who engage in unsafe driving practices. Driver behaviour profiles (DBPs) are introduced here as an approach for evaluating driver behaviour as a function of the Risk of a casualty crash. They employ data collected using global positioning system (GPS) devices, supplemented with spatiotemporal information. These profiles are comprised of common Risk scores that can be used to compare drivers between each other and across time and space. The paper details the development of these DBPs and demonstrates their use as an input into modelling the factors that influence driver behaviour. The results show that even having controlled for the influence of the road environment, these factors remain the strongest predictors of driver behaviour suggesting different spatiotemporal environments elicit a variety of psychological responses in drivers. The approach and outcomes will be of interest to insurance companies in enhancing the Risk-Profiling of drivers with on-road driving and government through assessing the impacts of behaviour-change interventions. Language: en
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evaluating changes in driver behaviour a Risk Profiling approach
Accident Analysis & Prevention, 2015Co-Authors: Adrian B Ellison, Michiel C J Bliemer, Stephen GreavesAbstract:New road safety strategies continue to be devised by researchers and policy makers with pay-as-you-drive (PAYD) schemes gaining increasing attention. However, empirically measuring the effectiveness of these strategies is challenging due to the influence of the road environment and other factors external to the driver. The analysis presented here applies Temporal and Spatial Identifiers to control for the road environment and Driver Behaviour Profiles to provide a common measure of driving behaviour based on the Risk of a casualty crash for assessing the effectiveness of a PAYD scheme on reducing driving Risks. The results show that in many cases personalised feedback alone is sufficient to induce significant changes, but the largest reductions in Risk are observed when drivers are also awarded a financial incentive to change behaviour. Importantly, the more frequent the exposure to the speeding information, the greater the magnitude of the change. However, the changes are disproportionately associated with those that were already safer drivers in the baseline period suggesting that some drivers may be predisposed to changing their behaviour. These results suggest that it would be beneficial to provide real-time or daily feedback on speeding behaviour in conjunction with a financial reward scheme, potentially as a component of insurance premiums. Language: en
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evaluating changes in driver behaviour a Risk Profiling approach
Accident Analysis & Prevention, 2015Co-Authors: Adrian B Ellison, Michiel C J Bliemer, Stephen GreavesAbstract:New road safety strategies continue to be devised by researchers and policy makers with pay-as-you-drive (PAYD) schemes gaining increasing attention. However, empirically measuring the effectiveness of these strategies is challenging due to the influence of the road environment and other factors external to the driver. The analysis presented here applies Temporal and Spatial Identifiers to control for the road environment and Driver Behaviour Profiles to provide a common measure of driving behaviour based on the Risk of a casualty crash for assessing the effectiveness of a PAYD scheme on reducing driving Risks. The results show that in many cases personalised feedback alone is sufficient to induce significant changes, but the largest reductions in Risk are observed when drivers are also awarded a financial incentive to change behaviour. Importantly, the more frequent the exposure to the speeding information, the greater the magnitude of the change. However, the changes are disproportionately associated with those that were already safer drivers in the baseline period suggesting that some drivers may be predisposed to changing their behaviour. These results suggest that it would be beneficial to provide real-time or daily feedback on speeding behaviour in conjunction with a financial reward scheme, potentially as a component of insurance premiums.
Yasuhiko Nakao - One of the best experts on this subject based on the ideXlab platform.
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circulating extracellular vesicles carrying sphingolipid cargo for the diagnosis and dynamic Risk Profiling of alcoholic hepatitis
Hepatology, 2021Co-Authors: Tejasav S Sehrawat, Juan Pablo Arab, Mengfei Liu, Pouya Amrollahi, Meihua Wan, Jia Fan, Yasuhiko NakaoAbstract:BACKGROUND AND AIMS Alcoholic hepatitis (AH) is diagnosed by clinical criteria, although several objective scores facilitate Risk stratification. Extracellular vesicles (EVs) have emerged as biomarkers for many diseases and are also implicated in the pathogenesis of AH. Therefore, we investigated whether plasma EV concentration and sphingolipid cargo could serve as diagnostic biomarkers for AH and inform prognosis to permit dynamic Risk Profiling of AH subjects. APPROACH AND RESULTS EVs were isolated and quantified from plasma samples from healthy controls, heavy drinkers, and subjects with end-stage liver disease (ESLD) attributed to cholestatic liver diseases and nonalcoholic steatohepatitis, decompensated alcohol-associated cirrhosis (AC), and AH. Sphingolipids were quantified by tandem mass spectroscopy. The median plasma EV concentration was significantly higher in AH subjects (5.38 × 1011 /mL) compared to healthy controls (4.38 × 1010 /mL; P < 0.0001), heavy drinkers (1.28 × 1011 /mL; P < 0.0001), ESLD (5.35 × 1010 /mL; P < 0.0001), and decompensated AC (9.2 × 1010 /mL; P < 0.0001) disease controls. Among AH subjects, EV concentration correlated with Model for End-Stage Liver Disease score. When EV counts were dichotomized at the median, survival probability for AH subjects at 90 days was 63.0% in the high-EV group and 90.0% in the low-EV group (log-rank P value = 0.015). Interestingly, EV sphingolipid cargo was significantly enriched in AH when compared to healthy controls, heavy drinkers, ESLD, and decompensated AC (P = 0.0001). Multiple sphingolipids demonstrated good diagnostic and prognostic performance as biomarkers for AH. CONCLUSIONS Circulating EV concentration and sphingolipid cargo signature can be used in the diagnosis and differentiation of AH from heavy drinkers, decompensated AC, and other etiologies of ESLD and predict 90-day survival permitting dynamic Risk Profiling.
