SHAPE: A Sample-adaptive Hierarchical Prediction Network for Medication
  Recommendation
Sicen Liu Xiaolong Wang JIngcheng Du Yongshuai Hou Xianbing Zhao Hui Xu Hui Wang Yang Xiang Buzhou Tang
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.