The invention relates to the technical field of clinical experiment
data processing, and discloses a clinical experiment
data analysis method based on
reinforcement learning, and the method comprises the steps: obtaining
time sequence physiological parameters and treatment scheme execution state parameters; performing hierarchical
feature extraction on the
time sequence physiological parameters, constructing a dynamic response
feature model, and generating an
initial treatment response evaluation model in combination with the execution state parameters; loading risk constraints and
curative effect indexes to generate a comprehensive evaluation model, and simulating to obtain strategy regulation and
control data; constructing and training a
reinforcement learning decision model, and generating a treatment path correction model and
initial treatment parameters; obtaining dynamic strategy adjustment information, and constructing a multi-stage optimization model for iterative optimization to obtain
optimal treatment path parameters; reasoning a
curative effect standard reaching probability based on a real-
time model, and adjusting a strategy to realize dynamic
curative effect matching. According to the method, the clinical data dynamic analysis and treatment scheme optimization capability is improved, and the method is suitable for precise medical scenes.