ODT - Optimal Decision Trees Algorithm
Implements a tree-based method specifically designed for
personalized medicine applications. By using genomic and
mutational data, 'ODT' efficiently identifies optimal drug
recommendations tailored to individual patient profiles. The
'ODT' algorithm constructs decision trees that bifurcate at
each node, selecting the most relevant markers (discrete or
continuous) and corresponding treatments, thus ensuring that
recommendations are both personalized and statistically robust.
This iterative approach enhances therapeutic decision-making by
refining treatment suggestions until a predefined group size is
achieved. Moreover, the simplicity and interpretability of the
resulting trees make the method accessible to healthcare
professionals. Includes functions for training the decision
tree, making predictions on new samples or patients, and
visualizing the resulting tree. For detailed insights into the
methodology, please refer to Gimeno et al. (2023)
<doi:10.1093/bib/bbad200>.