SHAPE: A Sample-adaptive Hierarchical Prediction Network for Medication Recommendation

TL;DR

SHAPE is a novel hierarchical network that adaptively models intra-visit relationships and variable longitudinal sequences for improved medication recommendation in complex healthcare scenarios.

Contribution

The paper introduces a compact intra-visit set encoder and a soft curriculum learning strategy for variable visit lengths, advancing medication prediction methods.

Findings

Outperforms state-of-the-art baselines on benchmark datasets.

Effectively models intra-visit medical event relationships.

Handles variable visit lengths with soft curriculum learning.

Abstract

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.