  Effectively medication recommendation with complex multimorbidity conditions
is a critical task in healthcare. Most existing works predicted medications
based on longitudinal records, which assumed the information transmitted
patterns of learning longitudinal sequence data are stable and intra-visit
medical events are serialized. However, the following conditions may have been
ignored: 1) A more compact encoder for intra-relationship in the intra-visit
medical event is urgent; 2) Strategies for learning accurate representations of
the variable longitudinal sequences of patients are different. In this paper,
we proposed a novel Sample-adaptive Hierarchical medicAtion Prediction nEtwork,
termed SHAPE, to tackle the above challenges in the medication recommendation
task. Specifically, we design a compact intra-visit set encoder to encode the
relationship in the medical event for obtaining visit-level representation and
then develop an inter-visit longitudinal encoder to learn the patient-level
longitudinal representation efficiently. To endow the model with the capability
of modeling the variable visit length, we introduce a soft curriculum learning
method to assign the difficulty of each sample automatically by the visit
length. Extensive experiments on a benchmark dataset verify the superiority of
our model compared with several state-of-the-art baselines.
