Cost-Effectiveness Analysis of Optimized Chemotherapy Treatments

Despite significant advances in medicine, cancer continues to claim millions of lives worldwide. However, with the rapid development of computer technology, a new avenue is emerging: the use of computer simulations and optimizations to create personalized treatments. This approach allows for the development of therapies that are not only more effective but also more cost-efficient, requiring less medication. In our research, we focus on optimizing chemotherapy treatments and evaluating them using economic indicators to assess cost-effectiveness. We generate therapies using a genetic algorithm and a multi-objective optimization algorithm. The multi-objective optimization is implemented using the epsilon-constraint method, which produces a Pareto front where optimal chemotherapy treatments are identified based on multiple criteria. The established chemotherapy protocols aim to improve clinical outcomes while reducing the economic burden on healthcare systems. Additionally, we compare these new therapies with the currently used standard clinical procedures. This comparison helps us understand the cost-effectiveness of our algorithms and whether it is worthwhile to implement them in clinical settings.