A novel approach for personalized response model: deep learning with individual dropout feature ranking.
Journal:
Journal of pharmacokinetics and pharmacodynamics
PMID:
33104924
Abstract
Deep learning is the fastest growing field in artificial intelligence and has led to many transformative innovations in various domains. However, lack of interpretability sometimes hinders its application in hypothesis-driven domains such as biology and healthcare. In this paper, we propose a novel deep learning model with individual feature ranking. Several simulated datasets with the scenarios that contributing features are correlated and buried among non-contributing features were used to characterize the novel analysis approach. A publicly available clinical dataset was also applied. The performance of the individual level dropout feature ranking model was compared with commonly used artificial neural network model, random forest model, and population level dropout feature ranking model. The individual level dropout feature ranking model provides a reasonable prediction of the outcomes. Unlike the random forest model and population level dropout feature ranking model, which can only identify global-wise contributing features (i.e., at population level), the individual level dropout feature ranking model allows further identification of impactful features on response at individual level. Therefore, it provides a basis for clustering patients into subgroups. This may provide a new tool for enriching patients in clinical drug development and developing personalized or individualized medicine.