Optimizing Treatment Plans for Infectious Diseases Using Sequential Models and Concurrent Optimization Algorithms

They can quickly manage, diagnose, and treat infectious diseases. If treatment plans fail to work, it can lead to prolonged illness, worse healthcare costs, or even death. Therefore, optimizing treatment regimens for infectious diseases is essential to enhance patient outcomes. This article proposes an approach that integrates sequential models with concurrent optimization algorithms to optimize treatment plans for contagion diseases. Sequential models apply machinelearning algorithms to patient data and predict the possible outcome of treatments. Subsequently, these predictions are input into constellations of person-centered algorithms that utilize mathematical optimization techniques to generate and assess different treatment plans simultaneously. Our method accepts sequential no and concurrent optimization algorithms to improve the accuracy and efficiency of treatment plan optimization. This is done by modeling infectious agent epidemic spread and response heterogeneity to treatment options among infected individuals. Our method expedites obtaining the optimal treatment plan by constructing and assessing various treatment plans.