TITLE:
Drug-Treatment Generation Combinatorial Algorithm Based on Machine Learning and Statistical Methodologies
AUTHORS:
Karen Gishyan
KEYWORDS:
Combinatorial Treatments, Health Informatics, Machine Learning
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.13 No.4,
April
28,
2023
ABSTRACT: Finding
out the desired drug combinations is a challenging task because of the number of different combinations that exist and
the adversarial effects that may arise. In this work, we generate drug
combinations over multiple stages using distance calculation metrics
from supervised learning, clustering, and a
statistical similarity calculation metric for deriving the optimal treatment
sequences. The combination generation happens for each patient based on the
characteristics (features) observed during each stage of treatment. Our
approach considers not the drug-to-drug (one-to-one) effect, but rather the
effect of group of drugs with another group of drugs. We evaluate the
combinations using an FNN model and identify future improvement
directions.