  To enhance therapeutic outcomes from a pharmacological perspective, we
propose MiranDa, designed for medication recommendation, which is the first
actionable model capable of providing the estimated length of stay in hospitals
(ELOS) as counterfactual outcomes that guide clinical practice and model
training. In detail, MiranDa emulates the educational trajectory of doctors
through two gradient-scaling phases shifted by ELOS: an Evidence-based Training
Phase that utilizes supervised learning and a Therapeutic Optimization Phase
grounds in reinforcement learning within the gradient space, explores optimal
medications by perturbations from ELOS. Evaluation of the Medical Information
Mart for Intensive Care III dataset and IV dataset, showcased the superior
results of our model across five metrics, particularly in reducing the ELOS.
Surprisingly, our model provides structural attributes of medication
combinations proved in hyperbolic space and advocated "procedure-specific"
medication combinations. These findings posit that MiranDa enhanced medication
efficacy. Notably, our paradigm can be applied to nearly all medical tasks and
those with information to evaluate predicted outcomes. The source code of the
MiranDa model is available at https://github.com/azusakou/MiranDa.
