With the growing imbalance between limited medical resources and escalating
demands, AI-based clinical tasks have become paramount. Medication
recommendation, as a sub-domain, aims to amalgamate longitudinal patient
history with medical knowledge, assisting physicians in prescribing safer and
more accurate medication combinations. Existing methods overlook the inherent
long-tail distribution in medical data, lacking balanced representation between
head and tail data, which leads to sub-optimal model performance. To address
this challenge, we introduce StratMed, a model that incorporates an innovative
relevance stratification mechanism. It harmonizes discrepancies in data
long-tail distribution and strikes a balance between the safety and accuracy of
medication combinations. Specifically, we first construct a pre-training method
using deep learning networks to obtain entity representation. After that, we
design a pyramid-like data stratification method to obtain more generalized
entity relationships by reinforcing the features of unpopular entities. Based
on this relationship, we designed two graph structures to express medication
precision and safety at the same level to obtain visit representations.
Finally, the patient's historical clinical information is fitted to generate
medication combinations for the current health condition. Experiments on the
MIMIC-III dataset demonstrate that our method has outperformed current
state-of-the-art methods in four evaluation metrics (including safety and
accuracy)