Improving mixed-integer temporal modeling by generating synthetic data using conditional generative adversarial networks: A case study of fluid overload prediction …
Objective The challenge of mixed-integer temporal data, which is particularly prominent for
medication use in the critically ill, limits the performance of predictive models. The purpose …
medication use in the critically ill, limits the performance of predictive models. The purpose …
Improving irregular temporal modeling by integrating synthetic data to the electronic medical record using conditional GANs: a case study of fluid overload prediction …
Objective The challenge of irregular temporal data, which is particularly prominent for
medication use in the critically ill, limits the performance of predictive models. The purpose …
medication use in the critically ill, limits the performance of predictive models. The purpose …
A common data model for the standardization of intensive care unit medication features
Objective Common data models provide a standard means of describing data for artificial
intelligence (AI) applications, but this process has never been undertaken for medications …
intelligence (AI) applications, but this process has never been undertaken for medications …
Augmenting mortality prediction with medication data and machine learning models
Background: In critically ill patients, complex relationships exist among patient disease
factors, medication management, and mortality. Considering the potential for nonlinear …
factors, medication management, and mortality. Considering the potential for nonlinear …
[PDF][PDF] Supplemental Digital Content–Table 2. Development and evaluation of the MRC-ICU
A II - a-sikora.com
A CDM for 889 ICU medications was developed and approved and includes three features:
1) drug product features (name, dose, formulation, route of administration), 2) clinical …
1) drug product features (name, dose, formulation, route of administration), 2) clinical …