The reported prevalence of autism spectrum disorder（ASD） has been increasing rapidly in many parts of the world. However, data on its prevalence in China are largely missing. Here, we assessed the suitability of the...The reported prevalence of autism spectrum disorder（ASD） has been increasing rapidly in many parts of the world. However, data on its prevalence in China are largely missing. Here, we assessed the suitability of the modi？ed Chinese version of a newly-developed ASD screening tool, the Modi？ed Chinese Autism Spectrum Rating Scales（MC-ASRS） in screening for ASD in Chi nese children aged 6–12 years, through comparison with the Social Responsiveness Scale（SRS） that has been widely used for ASD screening. We recruited the par ents/caregivers of 1588 typically-developing children and190 children with ASD aged 6–12 years to complete the MC-ASRS and SRS, and evaluated the validity of both scales in discriminating children with ASD from those developing typically. The results showed that MC-ASRSperformed as well as SRS in sensitivity, speci？city, and area-under-the-curve（both [0.95） in receiver operating characteristic analysis, with a fair false-negative rate.These results suggest that MC-ASRS is a promising tool for screening for children with ASD in the general Chinese population.展开更多
We postulate and analyze a nonlinear subsampling accuracy loss(SSAL)model based on the root mean square error(RMSE)and two SSAL models based on the mean square error(MSE),suggested by extensive preliminary simulations...We postulate and analyze a nonlinear subsampling accuracy loss(SSAL)model based on the root mean square error(RMSE)and two SSAL models based on the mean square error(MSE),suggested by extensive preliminary simulations.The SSAL models predict accuracy loss in terms of subsampling parameters like the fraction of users dropped(FUD)and the fraction of items dropped(FID).We seek to investigate whether the models depend on the characteristics of the dataset in a constant way across datasets when using the SVD collaborative filtering(CF)algorithm.The dataset characteristics considered include various densities of the rating matrix and the numbers of users and items.Extensive simulations and rigorous regression analysis led to empirical symmetrical SSAL models in terms of FID and FUD whose coefficients depend only on the data characteristics.The SSAL models came out to be multi-linear in terms of odds ratios of dropping a user(or an item)vs.not dropping it.Moreover,one MSE deterioration model turned out to be linear in the FID and FUD odds where their interaction term has a zero coefficient.Most importantly,the models are constant in the sense that they are written in closed-form using the considered data characteristics(densities and numbers of users and items).The models are validated through extensive simulations based on 850 synthetically generated primary(pre-subsampling)matrices derived from the 25M MovieLens dataset.Nearly 460000 subsampled rating matrices were then simulated and subjected to the singular value decomposition(SVD)CF algorithm.Further validation was conducted using the 1M MovieLens and the Yahoo!Music Rating datasets.The models were constant and significant across all 3 datasets.展开更多
The Autism Spectrum Rating Scale(ASRS) and the Social Responsiveness Scale(SRS) have been widely used for screening autism spectrum disorder(ASD) in the general population during epidemiological studies, but studies o...The Autism Spectrum Rating Scale(ASRS) and the Social Responsiveness Scale(SRS) have been widely used for screening autism spectrum disorder(ASD) in the general population during epidemiological studies, but studies of individuals with intellectual disability(ID) are quite limited. Therefore, we recruited the parents/caregivers of 204 ASD cases, 71 ID cases aged 6–18 years from special education schools, and 402 typically developing(TD) children in the same age span from a communitybased population to complete the ASRS and SRS. The results showed that the ID group scored significantly lower on total and subscale scores than the ASD group on both scales(P \ 0.05) but higher than TD children(P \ 0.05).Receiver operating characteristic analyses demonstrated a similar fair performance in discriminating ASD from ID with the ASRS(area under the curve(AUC) = 0.709,sensitivity = 77.0%, specificity = 52.1%, positive predictive value(PPV) = 82.2%) and the SRS(AUC = 0.742,sensitivity = 59.8%, specificity = 77.5%, PPV = 88.4%).The results showed that individuals with ID had clear autistic traits and discriminating ASD from ID cases was quite challenging, while assessment tools such as ASRS and SRS, help to some degree.展开更多
基金supported by the National Health and Family Planning Commission of China(201302002)the National Natural Science Foundation of China(81371270Clinical Trials.gov number NCT 02200679)
文摘The reported prevalence of autism spectrum disorder（ASD） has been increasing rapidly in many parts of the world. However, data on its prevalence in China are largely missing. Here, we assessed the suitability of the modi？ed Chinese version of a newly-developed ASD screening tool, the Modi？ed Chinese Autism Spectrum Rating Scales（MC-ASRS） in screening for ASD in Chi nese children aged 6–12 years, through comparison with the Social Responsiveness Scale（SRS） that has been widely used for ASD screening. We recruited the par ents/caregivers of 1588 typically-developing children and190 children with ASD aged 6–12 years to complete the MC-ASRS and SRS, and evaluated the validity of both scales in discriminating children with ASD from those developing typically. The results showed that MC-ASRSperformed as well as SRS in sensitivity, speci？city, and area-under-the-curve（both [0.95） in receiver operating characteristic analysis, with a fair false-negative rate.These results suggest that MC-ASRS is a promising tool for screening for children with ASD in the general Chinese population.
文摘We postulate and analyze a nonlinear subsampling accuracy loss(SSAL)model based on the root mean square error(RMSE)and two SSAL models based on the mean square error(MSE),suggested by extensive preliminary simulations.The SSAL models predict accuracy loss in terms of subsampling parameters like the fraction of users dropped(FUD)and the fraction of items dropped(FID).We seek to investigate whether the models depend on the characteristics of the dataset in a constant way across datasets when using the SVD collaborative filtering(CF)algorithm.The dataset characteristics considered include various densities of the rating matrix and the numbers of users and items.Extensive simulations and rigorous regression analysis led to empirical symmetrical SSAL models in terms of FID and FUD whose coefficients depend only on the data characteristics.The SSAL models came out to be multi-linear in terms of odds ratios of dropping a user(or an item)vs.not dropping it.Moreover,one MSE deterioration model turned out to be linear in the FID and FUD odds where their interaction term has a zero coefficient.Most importantly,the models are constant in the sense that they are written in closed-form using the considered data characteristics(densities and numbers of users and items).The models are validated through extensive simulations based on 850 synthetically generated primary(pre-subsampling)matrices derived from the 25M MovieLens dataset.Nearly 460000 subsampled rating matrices were then simulated and subjected to the singular value decomposition(SVD)CF algorithm.Further validation was conducted using the 1M MovieLens and the Yahoo!Music Rating datasets.The models were constant and significant across all 3 datasets.
基金supported by the National Health and Family Planning Commission of China (201302002 ClinicalTrials.gov Number NCT 02200679)
文摘The Autism Spectrum Rating Scale(ASRS) and the Social Responsiveness Scale(SRS) have been widely used for screening autism spectrum disorder(ASD) in the general population during epidemiological studies, but studies of individuals with intellectual disability(ID) are quite limited. Therefore, we recruited the parents/caregivers of 204 ASD cases, 71 ID cases aged 6–18 years from special education schools, and 402 typically developing(TD) children in the same age span from a communitybased population to complete the ASRS and SRS. The results showed that the ID group scored significantly lower on total and subscale scores than the ASD group on both scales(P \ 0.05) but higher than TD children(P \ 0.05).Receiver operating characteristic analyses demonstrated a similar fair performance in discriminating ASD from ID with the ASRS(area under the curve(AUC) = 0.709,sensitivity = 77.0%, specificity = 52.1%, positive predictive value(PPV) = 82.2%) and the SRS(AUC = 0.742,sensitivity = 59.8%, specificity = 77.5%, PPV = 88.4%).The results showed that individuals with ID had clear autistic traits and discriminating ASD from ID cases was quite challenging, while assessment tools such as ASRS and SRS, help to some degree.
