Methodology and Assessment of Proxy-Based Vs30 Estimation in Sichuan Province, China

Wen, Wu; Liu, Jifu; Guo, Lanlan; Deng, Zhifei

doi:10.1007/s13753-020-00253-2

Cited by 9 publications

(5 citation statements)

References 10 publications

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“…Furthermore, we envisage that this strategy will assists in determining treatment methods with potentially excellent economic outcomes, to ensure higher patient bene ts at a lower cost. In addition, encouraging the rational use of medical resources would contribute to considerably conserving social investments and funds, while promoting the rational allocation of medical and health resources 15 . Therefore, economic aspects will also be an important focus of our next research studies.…”

Section: Urgency Of Is Treatment and Social Burdenmentioning

confidence: 99%

Precise thrombolytic drug treatments for stroke based on artificial intelligence：based on wide and deep model

Shen

Lin

Chen

et al. 2023

Preprint

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This study aimed to establish an artificial intelligence algorithm model for patients with Ischemic stroke(IS) based on general patient characteristics, drug treatment methods, and recovery effects. We sought to achieve individualized precise thrombolytic therapy and provide data support for clinical drug decision-making. Data of 55621 patients diagnosed with IS were extracted from the big data server of Yidu Cloud. After screening using the inclusion and exclusion criteria, 1236 and 619 patients were included in the neurological improvement and control groups, respectively for a total of 1855. Single-factor screening was conducted for different influencing factors within the scope of the investigation. Principal component analysis was used to reduce the influencing factor dimensions. We built logistic, support vector machine, C5.0 decision tree arithmetic, deep neural network, and wide and deep models. We further evaluated these models to compare their prediction performance, determine the best model algorithm, and identify the best model parameters. Feature engineering was used to construct a simplified model and evaluate its accuracy. Patient data extracted from another hospital in Dalian were used for external model validation. We included 1855 patients and 26 patient characteristics were included in the model through single-factor screening. The dimensions were reduced to two principal components principal component analysis, and the cumulative variance contribution rate was 93.1%. The wide and deep models showed the best prediction performance with an accuracy and F-index of 81.5% and 87.1%, respectively. Furthermore, the area under the receiver operating characteristic curve values of the training and test set were 0.753 and 0.793, respectively. The number of hidden layers and neurons in each layer of the model was seven and 15, respectively. Using Sigmoid as the activation function showed that the model parameters were optimal. Feature-engineering treatment showed that the accuracy of the model was 81.9%, and that of the external verification model after model simplification was 80.1%. The external verification accuracy of the wide and deep models was 75%, indicating good prediction performance and generalizability. The evaluation indexes of the wide and deep models were excellent. The artificial intelligence algorithm enabled the timely and effective provision of support for drug thrombolytic treatment planning for clinicians and references for clinical decision-making. Furthermore, the algorithm could potentially improve patient treatment outcomes, achieve personalized and accurate treatment for IS patients, and positively contribute to significantly reducing the social burden of disease.

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Section: Urgency Of Is Treatment and Social Burdenmentioning

confidence: 99%

Precise thrombolytic drug treatments for stroke based on artificial intelligence：based on wide and deep model

Shen

Lin

Chen

et al. 2023

Preprint

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“…While this is a useful model in that it allows us to consider the effect of site conditions on risk at a national level, the slope proxy for V S 30 does not perform well in regions that have undergone previous glaciation (e.g. Cannon and Dutta, 2015; Wu et al, 2020) or volcanic environments. Because the former pertains to large portions of Canada, the accuracy of this model could be greatly improved using regional models or by including terrain-based corrections to V S 30 proxies (e.g.…”

Section: Input Data Setsmentioning

confidence: 99%

A national seismic risk model for Canada: Methodology and scientific basis

et al. 2023

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Canada is exposed to rare but potentially destructive earthquakes that threaten densely settled metropolitan centers in many parts of the country. To assess the impacts and consequences of future natural-hazard events and help advance policy goals and objectives of the Sendai Framework for Disaster Risk Reduction, Natural Resources Canada, through a collaborative partnership with the Global Earthquake Model Foundation, produced a national seismic risk model. Developing this model has required the creation of a national exposure inventory, Canadian-specific fragility and vulnerability curves, and significant simplification of the Canadian Seismic Hazard Model which forms the basis for the design seismic hazard values of the National Building Code of Canada. Using the Global Earthquake Model Foundation’s OpenQuake Engine, probabilistic stochastic risk modeling is completed under baseline and simulated retrofit conditions to assess seismic risk at the neighborhood level for all settled areas in Canada. Output risk metrics include the expected immediate physical impacts of earthquake events such as building damage, casualties, and direct economic losses. This article documents the technical details of the modeling approach including a description of novel data sets in use, a summary of the extensive sensitivity testing undertaken, and characterization of quality control implemented in the absence of usable validating earthquake loss data. The results from this model, such as loss exceedance curves and annual average losses, provide an open, accessible and quantitative base of evidence for decision-making at local, regional, and national levels. As a large country with a complex seismic hazard model and dispersed populations, this Canadian study is unique. However, the challenges faced and solutions offered are likely to be of interest to other nations pursuing similar programs.

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“…Vs profiles, which are obtained by inverting Rayleigh wave dispersion curves, are used to determine Vs30 [46]. Vs30 values were calculated in this study using [47,48]…”

Section: Vs30 Shear Modulus (G30max) and Modulus Of Elasticity (E30) ...mentioning

confidence: 99%

Multichannel Analysis of Surface Waves (MASW) to Estimate the Shear Wave Velocity for Engineering Characterization of Soils at Hawassa Town, Southern Ethiopia

Ayele

Woldearegay

Meten

2022

International Journal of Geophysics

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Site characterization is a prerequisite for the successful and economic design of engineering structures and earthworks by providing geological information for any proposed project. Until now, no detail study has been carried out on the site characterization and classification using shear wave velocity (Vs) up to the top 30 m depth in Hawassa town. For this study, multichannel analysis of surface waves (MASW) was used to determine the variation of Vs for a proper civil engineering design in the town. In addition, vertical electrical sounding (VES) and standard penetration test (SPT) were employed to characterize the near-surface materials. The Vs30 map was prepared for Hawassa town using the estimated Vs30 values which ranges from 248.9 m/s to 371.3 m/s while the SPT-N values were ranges from 5bpf to 50bpf. The correlation of Vs and SPT-N values has been done by considering both corrected and uncorrected SPT-N values. The VES result showed that groundwater is found at a shallow depth. The correlation of Vs and SPT-N value was validated using regression model. The 1D Vs profile and 2D cross-section showed low Vs at a shallow depth. The near-surface soils of the town are classified based on the Vs30 as site class C (stiff soil and soft rock) and D class (stiff soils) according to the NEHRP (Natural Earthquake hazards Reduction Program) and as subsoil classes B and C according to the Eurocode 8. The geotechnical tests also showed that the soils in the study area are silty sand, sand and silty sand with some gravel. The low Vs values observed at a shallow depth should be given much attention during foundation design for the stability of civil engineering structures.

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Methodology and Assessment of Proxy-Based Vs30 Estimation in Sichuan Province, China

Cited by 9 publications

References 10 publications

Precise thrombolytic drug treatments for stroke based on artificial intelligence：based on wide and deep model

Precise thrombolytic drug treatments for stroke based on artificial intelligence：based on wide and deep model

A national seismic risk model for Canada: Methodology and scientific basis

Multichannel Analysis of Surface Waves (MASW) to Estimate the Shear Wave Velocity for Engineering Characterization of Soils at Hawassa Town, Southern Ethiopia

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