  Recent years have witnessed the rapid accumulation of massive electronic
medical records (EMRs), which highly support the intelligent medical services
such as drug recommendation. However, prior arts mainly follow the traditional
recommendation strategies like collaborative filtering, which usually treat
individual drugs as mutually independent, while the latent interactions among
drugs, e.g., synergistic or antagonistic effect, have been largely ignored. To
that end, in this paper, we target at developing a new paradigm for drug
package recommendation with considering the interaction effect within drugs, in
which the interaction effects could be affected by patient conditions.
Specifically, we first design a pre-training method based on neural
collaborative filtering to get the initial embedding of patients and drugs.
Then, the drug interaction graph will be initialized based on medical records
and domain knowledge. Along this line, we propose a new Drug Package
Recommendation (DPR) framework with two variants, respectively DPR on Weighted
Graph (DPR-WG) and DPR on Attributed Graph (DPR-AG) to solve the problem, in
which each the interactions will be described as signed weights or attribute
vectors. In detail, a mask layer is utilized to capture the impact of patient
condition, and graph neural networks (GNNs) are leveraged for the final graph
induction task to embed the package. Extensive experiments on a real-world data
set from a first-rate hospital demonstrate the effectiveness of our DPR
framework compared with several competitive baseline methods, and further
support the heuristic study for the drug package generation task with adequate
performance.
