  Recommending medications for patients using electronic health records (EHRs)
is a crucial data mining task for an intelligent healthcare system. It can
assist doctors in making clinical decisions more efficiently. However, the
inherent complexity of the EHR data renders it as a challenging task: (1)
Multilevel structures: the EHR data typically contains multilevel structures
which are closely related with the decision-making pathways, e.g., laboratory
results lead to disease diagnoses, and then contribute to the prescribed
medications; (2) Multiple sequences interactions: multiple sequences in EHR
data are usually closely correlated with each other; (3) Abundant noise: lots
of task-unrelated features or noise information within EHR data generally
result in suboptimal performance. To tackle the above challenges, we propose a
multilevel selective and interactive network (MeSIN) for medication
recommendation. Specifically, MeSIN is designed with three components. First,
an attentional selective module (ASM) is applied to assign flexible attention
scores to different medical codes embeddings by their relevance to the
recommended medications in every admission. Second, we incorporate a novel
interactive long-short term memory network (InLSTM) to reinforce the
interactions of multilevel medical sequences in EHR data with the help of the
calibrated memory-augmented cell and an enhanced input gate. Finally, we employ
a global selective fusion module (GSFM) to infuse the multi-sourced information
embeddings into final patient representations for medications recommendation.
To validate our method, extensive experiments have been conducted on a
real-world clinical dataset. The results demonstrate a consistent superiority
of our framework over several baselines and testify the effectiveness of our
proposed approach.
