Nina Deliu
Datos Biográficos
| ID | 6062803 |
|---|---|
| NOMBRE | Nina Deliu |
| NOMBRES | Nina |
| APELLIDO | Deliu |
| FIRMA | DELIU N |
| AFILIACIONES | University of Cambridge |
| ORCID | 0000-0003-2501-8795 |
| VERIFICADO | Sí |
| TOTAL DE OBRAS | 3 |
| TOTAL DE CITAS | 0 |
| OBRAS COMO AUTOR | 3 |
| OBRAS COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 2019 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2024 |
| ÍNDICE H | 0 |
Reinforcement learning for sequential decision making in population research
Reinforcement learning (RL) algorithms have been long recognized as powerful tools for optimal sequential decision making. The framework is concerned with a decision maker, the agent, that learns how to behave in an unknown environment by making decisions and seeing their associated outcome. The goal of the RL agent is to infer, through repeated experience, an optimal decision-making policy, i.e., a sequence of action rules that would lead to the…
Microrandomized Trials
Just-in-time adaptive interventions (JITAIs) represent an intervention design that adapts the provision and type of support over time to an individual’s changing status and contexts, intending to deliver the right support on the right occasion. As a novel strategy for delivering mobile health interventions, JITAIs have the potential to improve access to quality care in underserved communities and, thus, alleviate health disparities, a significant…
Modeling strategies to improve parameter estimates in prognostic factors analyses with patient-reported outcomes in oncology
Sin obras prominentes en esta página.
Modeling strategies to improve parameter estimates in prognostic factors analyses with patient-reported outcomes in oncology
Microrandomized Trials
Just-in-time adaptive interventions (JITAIs) represent an intervention design that adapts the provision and type of support over time to an individual’s changing status and contexts, intending to deliver the right support on the right occasion. As a novel strategy for delivering mobile health interventions, JITAIs have the potential to improve access to quality care in underserved communities and, thus, alleviate health disparities, a significant…
Reinforcement learning for sequential decision making in population research
Reinforcement learning (RL) algorithms have been long recognized as powerful tools for optimal sequential decision making. The framework is concerned with a decision maker, the agent, that learns how to behave in an unknown environment by making decisions and seeing their associated outcome. The goal of the RL agent is to infer, through repeated experience, an optimal decision-making policy, i.e., a sequence of action rules that would lead to the…
Medicine (2 obras) · Action (physics) (1 obra) · Akaike information criterion (1 obra) · Applied Psychology (1 obra) · Artificial Intelligence (1 obra) · Artificial Intelligence (1 obra) · Clinical study design (1 obra) · Clinical trial (1 obra) · Colorectal Cancer Screening and Detection (1 obra) · Computer Science (1 obra)