Andi Tan
Biographic Data
| ID | 7896410 |
|---|---|
| NAME | Andi Tan |
| GIVEN NAMES | Andi |
| FAMILY NAME | Tan |
| SIGNATURE | TAN A |
| AFFILIATIONS | Yunnan University of Finance And Economics |
| ORCID | 0000-0003-0666-1312 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 0 |
Association between blood lead levels and hyperlipidemiais
Background Research on the association between blood lead (Pb) and lipid biomarkers have yielded inconsistent results, and epidemiological studies on blood Pb levels and hyperlipidemia are scarce. The present study aimed to examine the association between blood Pb levels and hyperlipidemia in adults from the National Health and Nutrition Examination Survey (NHANES). Methods A total of 43,196 participants in the NHANES from 1999 to 2018 were inclu…
A Hybrid Model for Coronavirus Disease 2019 Forecasting Based on Ensemble Empirical Mode Decomposition and Deep Learning
The novel coronavirus pneumonia that began to spread in 2019 is still raging and has placed a burden on medical systems and governments in various countries. For policymaking and medical resource decisions, a good prediction model is necessary to monitor and evaluate the trends of the epidemic. We used a long short-term memory (LSTM) model and the improved hybrid model based on ensemble empirical mode decomposition (EEMD) to predict COVID-19 tren…
No prominent works on this page.
Association between blood lead levels and hyperlipidemiais
Background Research on the association between blood lead (Pb) and lipid biomarkers have yielded inconsistent results, and epidemiological studies on blood Pb levels and hyperlipidemia are scarce. The present study aimed to examine the association between blood Pb levels and hyperlipidemia in adults from the National Health and Nutrition Examination Survey (NHANES). Methods A total of 43,196 participants in the NHANES from 1999 to 2018 were inclu…
A Hybrid Model for Coronavirus Disease 2019 Forecasting Based on Ensemble Empirical Mode Decomposition and Deep Learning
The novel coronavirus pneumonia that began to spread in 2019 is still raging and has placed a burden on medical systems and governments in various countries. For policymaking and medical resource decisions, a good prediction model is necessary to monitor and evaluate the trends of the epidemic. We used a long short-term memory (LSTM) model and the improved hybrid model based on ensemble empirical mode decomposition (EEMD) to predict COVID-19 tren…
Medicine (2 works) · Artificial Intelligence (1 work) · Artificial neural network (1 work) · Computer Science (1 work) · Coronavirus disease 2019 (COVID-19 (1 work) · COVID-19 diagnosis using AI (1 work) · COVID-19 epidemiological studies (1 work) · Disease (1 work) · Ensemble forecasting (1 work) · Environmental health (1 work)