Matías García-Constantino
Datos Biográficos
| ID | 7758971 |
|---|---|
| NOMBRE | Matías García-Constantino |
| NOMBRES | Matías |
| APELLIDO | García-Constantino |
| FIRMA | GARCÍA-CONSTANTINO M |
| AFILIACIONES | University of Ulster |
| ORCID | 0000-0002-3420-0532 |
| VERIFICADO | Sí |
| TOTAL DE OBRAS | 2 |
| TOTAL DE CITAS | 0 |
| OBRAS COMO AUTOR | 2 |
| OBRAS COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 2021 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2025 |
| ÍNDICE H | 0 |
Comparison of spatial prediction models from Machine Learning of cholangiocarcinoma incidence in Thailand
The Random Forest model demonstrated the highest efficiency in predicting CCA incidence rates in Thailand, though predictive performance varied across regions. Spatial factors effectively predicted ASR of CCA, providing valuable insights for national-level disease surveillance and targeted public health interventions. These findings support the development of region-specific approaches for CCA control using spatial epidemiology and machine learni…
The “Worktivity” mHealth intervention to reduce sedentary behaviour in the workplace
The findings suggest that, in principle, it is feasible to implement a mobile app-based intervention in the workplace setting however the Worktivity intervention requires further technical refinements before moving to effectiveness trials. Challenges relating to the initial recruitment of workplaces and maintaining user engagement with the mHealth intervention over time need to be addressed prior to future large-scale implementation. Further rese…
Sin obras prominentes en esta página.
The “Worktivity” mHealth intervention to reduce sedentary behaviour in the workplace
The findings suggest that, in principle, it is feasible to implement a mobile app-based intervention in the workplace setting however the Worktivity intervention requires further technical refinements before moving to effectiveness trials. Challenges relating to the initial recruitment of workplaces and maintaining user engagement with the mHealth intervention over time need to be addressed prior to future large-scale implementation. Further rese…
Comparison of spatial prediction models from Machine Learning of cholangiocarcinoma incidence in Thailand
The Random Forest model demonstrated the highest efficiency in predicting CCA incidence rates in Thailand, though predictive performance varied across regions. Spatial factors effectively predicted ASR of CCA, providing valuable insights for national-level disease surveillance and targeted public health interventions. These findings support the development of region-specific approaches for CCA control using spatial epidemiology and machine learni…
Biostatistics (2 obras) · Medicine (2 obras) · Public health (2 obras) · Cholangiocarcinoma and Gallbladder Cancer Studies (1 obra) · Cluster (spacecraft (1 obra) · Cluster randomised controlled trial (1 obra) · Environmental health (1 obra) · Epidemiology (1 obra) · Gallbladder and Bile Duct Disorders (1 obra) · Incidence (geometry (1 obra)