Ta-Chou Ng
Datos Biográficos
| ID | 6069293 |
|---|---|
| NOMBRE | Ta-Chou Ng |
| NOMBRES | Ta-Chou |
| APELLIDO | Ng |
| FIRMA | NG T |
| AFILIACIONES | Ta-Chou Ng, Wei-Cheng Lo, and Hsien-Ho Lin are with the Graduate Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan. Wei-Cheng Lo is also with the Institute of Statistical Science, Academia Sinica, Taipei, Taiwan. Chu-Chang Ku is with the School of Health and Related Research, University of Sheffield, Sheffield, UK. Tsung-Hsueh Lu is with the National Cheng Kung University Research Center for Health Data and Department of Public Health,... |
| VERIFICADO | No |
| TOTAL DE OBRAS | 1 |
| TOTAL DE CITAS | 1 |
| OBRAS COMO AUTOR | 1 |
| OBRAS COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 2020 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2020 |
| ÍNDICE H | 1 |
Improving the Use of Mortality Data in Public Health
Objectives. To describe and compare 3 garbage code (GC) redistribution models: naïve Bayes classifier (NB), coarsened exact matching (CEM), and multinomial logistic regression (MLR). Methods. We analyzed Taiwan Vital Registration data (2008–2016) using a 2-step approach. First, we used non-GC death records to evaluate 3 different prediction models (NB, CEM, and MLR), incorporating individual-level information on multiple causes of death (MCDs) an…
Improving the Use of Mortality Data in Public Health
Objectives. To describe and compare 3 garbage code (GC) redistribution models: naïve Bayes classifier (NB), coarsened exact matching (CEM), and multinomial logistic regression (MLR). Methods. We analyzed Taiwan Vital Registration data (2008–2016) using a 2-step approach. First, we used non-GC death records to evaluate 3 different prediction models (NB, CEM, and MLR), incorporating individual-level information on multiple causes of death (MCDs) an…
Improving the Use of Mortality Data in Public Health
Objectives. To describe and compare 3 garbage code (GC) redistribution models: naïve Bayes classifier (NB), coarsened exact matching (CEM), and multinomial logistic regression (MLR). Methods. We analyzed Taiwan Vital Registration data (2008–2016) using a 2-step approach. First, we used non-GC death records to evaluate 3 different prediction models (NB, CEM, and MLR), incorporating individual-level information on multiple causes of death (MCDs) an…
Artificial Intelligence (1 obra) · Artificial Intelligence (1 obra) · Autopsy Techniques and Outcomes (1 obra) · Bayes' theorem (1 obra) · Bayesian probability (1 obra) · Computer Science (1 obra) · Health disparities and outcomes (1 obra) · Insurance, Mortality, Demography, Risk Management (1 obra) · Logistic regression (1 obra) · Machine learning (1 obra)