Yi-Hau Chen
Biographic Data
| ID | 5541885 |
|---|---|
| NAME | Yi-Hau Chen |
| GIVEN NAMES | Yi-Hau |
| FAMILY NAME | Chen |
| SIGNATURE | CHEN Y |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2009 |
| LATEST PUBLICATION YEAR | 2021 |
| H-INDEX | 0 |
The Optimal Machine Learning-Based Missing Data Imputation for the Cox Proportional Hazard Model
An adequate imputation of missing data would significantly preserve the statistical power and avoid erroneous conclusions. In the era of big data, machine learning is a great tool to infer the missing values. The root means square error (RMSE) and the proportion of falsely classified entries (PFC) are two standard statistics to evaluate imputation accuracy. However, the Cox proportional hazards model using various types requires deliberate study,…
Associations of Physician Volume and Weekend Admissions With Ischemic Stroke Outcome in Taiwan
BACKGROUND: Although volume-outcome and weekend-outcome relationships have been explored for various procedures and interventions, limited information is available concerning "physician volume" and the "weekend effect" on stroke mortality. Moreover, little is known about the relative and combined influence of physician and hospital volume on stroke mortality. OBJECTIVES: We used nationwide population-based data to explore the influences of physic…
No prominent works on this page.
Associations of Physician Volume and Weekend Admissions With Ischemic Stroke Outcome in Taiwan
BACKGROUND: Although volume-outcome and weekend-outcome relationships have been explored for various procedures and interventions, limited information is available concerning "physician volume" and the "weekend effect" on stroke mortality. Moreover, little is known about the relative and combined influence of physician and hospital volume on stroke mortality. OBJECTIVES: We used nationwide population-based data to explore the influences of physic…
The Optimal Machine Learning-Based Missing Data Imputation for the Cox Proportional Hazard Model
An adequate imputation of missing data would significantly preserve the statistical power and avoid erroneous conclusions. In the era of big data, machine learning is a great tool to infer the missing values. The root means square error (RMSE) and the proportion of falsely classified entries (PFC) are two standard statistics to evaluate imputation accuracy. However, the Cox proportional hazards model using various types requires deliberate study,…
Acute Ischemic Stroke Management (1 works) · Artificial Intelligence (1 works) · Computer Science (1 works) · Data mining (1 works) · Emergency Medicine (1 works) · Emergency Medicine (1 works) · Family medicine (1 works) · Healthcare Operations and Scheduling Optimization (1 works) · Hospital Admissions and Outcomes (1 works) · Internal Medicine (1 works)