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- Li, Wei, et al.
(författare)
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Non-lab and semi-lab algorithms for screening undiagnosed diabetes : A cross-sectional study
- 2018
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Ingår i: EBioMedicine. - : ELSEVIER SCIENCE BV. - 2352-3964. ; 35, s. 307-316
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Tidskriftsartikel (refereegranskat)abstract
- Background: The terrifying undiagnosed rate and high prevalence of diabetes have become a public emergency. A high efficiency and cost-effective early recognition method is urgently needed. We aimed to generate innovative, user-friendly nomograms that can be applied for diabetes screening in different ethnic groups in China using the non-lab or noninvasive semi-lab data. Methods: This multicenter, multi-ethnic, population-based, cross-sectional study was conducted in eight sites in China by enrolling subjects aged 20-70. Sociodemographic and anthropometric characteristics were collected. Blood and urine samples were obtained 2 h following a standard 75 g glucose solution. In the final analysis, 10,794 participants were included and randomized into model development (n - 8096) and model validation (n = 2698) group with a ratio of 3:1. Nomograms were developed by the stepwise binary logistic regression. The nomograms were validated internally by a bootstrap sampling method in the model development set and externally in the model validation set. The area under the receiver operating characteristic curve (AUC) was used to assess the screening performance of the nomograms. Decision curve analysis was applied to calculate the net benefit of the screening model. Results: The overall prevalence of undiagnosed diabetes was 9.8% (1059/10794) according to ADA criteria. The non-lab model revealed that gender, age, body mass index, waist circumference, hypertension, ethnicities, vegetable daily consumption and family history of diabetes were independent risk factors for diabetes. By adding 2 h post meal glycosuria qualitative to the non-lab model, the semi-lab model showed an improved Akaike information criterion (AIC: 4506 to 3580). The AUC of the semi-lab model was statistically larger than the non-lab model (0.868 vs 0.763, P < 0.001). The optimal cutoff probability in semi-lab and non-lab nomograms were 0.088 and 0.098, respectively. The sensitivity and specificity were 76.3% and 81.6%, respectively in semi-lab nomogram, and 72.1% and 673% in non-lab nomogram at the optimal cut off point. The decision curve analysis also revealed a bigger decrease of avoidable OGTT test (52 per 100 subjects) in the semi-lab model compared to the non-lab model (36 per 100 subjects) and the existed New Chinese Diabetes Risk Score (NCDRS, 35 per 100 subjects). Conclusion: The non-lab and semi-lab nomograms appear to be reliable tools for diabetes screening, especially in developing countries. However, the semi-lab model outperformed the non-lab model and NCDRS prediction systems and might be worth being adopted as decision support in diabetes screening in China.
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| 2. |
- Zhang, Pengcheng, et al.
(författare)
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Geospatial learning for large-scale transport infrastructure depth prediction
- 2024
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier. - 1569-8432 .- 1872-826X. ; 132
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Tidskriftsartikel (refereegranskat)abstract
- Transportation infrastructure supports the smooth mobility of humans, commodities, and services. Pavement depth measures the quality of road infrastructure through representing the thickness of road surfaces, and influences various aspects of construction projects. However, accurately modeling and predicting pavement depth has been a critical challenge due to diverse and complex factors, such as weather dynamics, traffic patterns, maintenance interventions, and environmental fluctuations. This study develops a second-dimension spatial learning (SDSL) model that integrates geospatial models and machine learning for large-scale pavement depth prediction. SDSL models are implemented in pavement prediction for eight distinct regions in Western Australia, and they are validated using the observation of pavement depth through cross-validation. Results demonstrate that the proposed SDSL models can more accurately predict large-scale pavement depth than the existing first-dimension spatial learning (FDSL) models, with 17.3% to 37.6% increase of R2 values, 1.46% to 16.5% reduction of RMSE, 1.7% to 31.1% reduction of MAE and 21.0% reduction of prediction uncertainty. SDSL models enhance effective infrastructure management by accurately predicting pavement depth, essential for maintaining large-scale transportation infrastructure. The study significantly contributes to the efficient management of sustainable infrastructure assets, saving time and money. © 2024 The Authors
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