Ta-Chou Vincent Ng
Biographic Data
| ID | 10877992 |
|---|---|
| NAME | Ta-Chou Vincent Ng |
| GIVEN NAMES | Ta-Chou Vincent |
| FAMILY NAME | Ng |
| SIGNATURE | NG T V |
| AFFILIATIONS | National Chengchi University |
| ORCID | 0000-0003-1446-5864 |
| VERIFIED | No |
| TOTAL WORKS | 1 |
| TOTAL CITATIONS | 1 |
| WORKS AS AUTHOR | 1 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2020 |
| H-INDEX | 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 work) · Artificial Intelligence (1 work) · Autopsy Techniques and Outcomes (1 work) · Bayes' theorem (1 work) · Bayesian probability (1 work) · Computer Science (1 work) · Health disparities and outcomes (1 work) · Insurance, Mortality, Demography, Risk Management (1 work) · Logistic regression (1 work) · Machine learning (1 work)