  Sepsis is a life-threatening medical emergency, which is a major cause of
death worldwide and the second highest cause of mortality in the United States.
Researching the optimal control treatment or intervention strategy on the
comprehensive sepsis system is key in reducing mortality. For this purpose,
first, this paper improves a complex nonlinear sepsis model proposed in our
previous work. Then, bifurcation analyses are conducted for each sepsis
subsystem to study the model behaviors under some system parameters. The
bifurcation analysis results also further indicate the necessity of control
treatment and intervention therapy. If the sepsis system is without adding any
control under some parameter and initial system value settings, the system will
perform persistent inflammation outcomes as time goes by. Therefore, we develop
our complex improved nonlinear sepsis model into a sepsis optimal control
model, and then use some effective biomarkers recommended in existing clinic
practices as optimization objective function to measure the development of
sepsis. Besides that, a Bayesian optimization algorithm by combining Recurrent
neural network (RNN-BO algorithm) is introduced to predict the optimal control
strategy for the studied sepsis optimal control system. The difference between
the RNN-BO algorithm from other optimization algorithms is that once given any
new initial system value setting (initial value is associated with the initial
conditions of patients), the RNN-BO algorithm is capable of quickly predicting
a corresponding time-series optimal control based on the historical optimal
control data for any new sepsis patient. To demonstrate the effectiveness and
efficiency of the RNN-BO algorithm on solving the optimal control solution on
the complex nonlinear sepsis system, some numerical simulations are implemented
by comparing with other optimization algorithms in this paper.
