Pharmaceutics, Free Full-Text
Por um escritor misterioso
Descrição
Exposure-response (E-R) is a key aspect of pharmacometrics analysis that supports drug dose selection. Currently, there is a lack of understanding of the technical considerations necessary for drawing unbiased estimates from data. Due to recent advances in machine learning (ML) explainability methods, ML has garnered significant interest for causal inference. To this end, we used simulated datasets with known E-R “ground truth” to generate a set of good practices for the development of ML models required to avoid introducing biases when performing causal inference. These practices include the use of causal diagrams to enable the careful consideration of model variables by which to obtain desired E-R relationship insights, keeping a strict separation of data for model-training and for inference generation to avoid biases, hyperparameter tuning to improve the reliability of models, and estimating proper confidence intervals around inferences using a bootstrap sampling with replacement strategy. We computationally confirm the benefits of the proposed ML workflow by using a simulated dataset with nonlinear and non-monotonic exposure–response relationships.
Pharma_Edu_Official
Empty Pharmacy Shelves Image & Photo (Free Trial)
PSOAR & PGIAR: The Blog & Custom Search Engines: International
PDF) PHARMACEUTICS-I
SOLUTION: Ebooksclub org handbook of pharmaceutical manufacturing
CanadaCSPS (@CanadaCSPS) / X
The IPhO Podcast a podcast by IPhO
Personalized Pharmaceutical Credit Card Flash Drive - 16 GB
Hawaii Pharm Psyllium (Plantago Psyllium) Liquid
Understanding Pharma: The Professional's Guide to How
AAPS PharmSciTech
de
por adulto (o preço varia de acordo com o tamanho do grupo)