SHAPE: A Sample-Adaptive Hierarchical Prediction Network for Medication Recommendation.

Effectively medication recommendation with complex multimorbidity conditions is a critical yet challenging task in healthcare. Most existing works predicted medications based on longitudinal records, which assumed the encoding format of intra-visit medical events are serialized and information transmitted patterns of learning longitudinal sequence data are stable. 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 article, 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.

Hierarchical Molecular Graph Self-Supervised Learning for property prediction.

Molecular graph representation learning has shown considerable strength in molecular analysis and drug discovery. Due to the difficulty of obtaining molecular property labels, pre-training models based on self-supervised learning has become increasingly popular in molecular representation learning. Notably, Graph Neural Networks (GNN) are employed as the backbones to encode implicit representations of molecules in most existing works. However, vanilla GNN encoders ignore chemical structural information and functions implied in molecular motifs, and obtaining the graph-level representation via the READOUT function hinders the interaction of graph and node representations. In this paper, we propose Hierarchical Molecular Graph Self-supervised Learning (HiMol), which introduces a pre-training framework to learn molecule representation for property prediction. First, we present a Hierarchical Molecular Graph Neural Network (HMGNN), which encodes motif structure and extracts node-motif-graph hierarchical molecular representations. Then, we introduce Multi-level Self-supervised Pre-training (MSP), in which corresponding multi-level generative and predictive tasks are designed as self-supervised signals of HiMol model. Finally, superior molecular property prediction results on both classification and regression tasks demonstrate the effectiveness of HiMol. Moreover, the visualization performance in the downstream dataset shows that the molecule representations learned by HiMol can capture chemical semantic information and properties.

Boosting lesion annotation via aggregating explicit relations in external medical knowledge graph.

Predicting a comprehensive set of relevant labels on chest X-ray images faces great challenges towards bridging visual and textual modalities. Despite the success of Graph Convolutional Networks (GCN) on modeling label dependencies using co-occurrence matrix generated from dataset, they still suffer from inherent label imbalance in dataset and ignore the explicit relations among labels presented in external medical knowledge graph (KG). We argue that jointly exploiting both the label co-occurrence matrix in dataset and the label relations in external knowledge graph facilitates multi-label lesion annotation. To model relevant lesion labels more comprehensively, we propose a KG-augmented model via Aggregating Explicit Relations for multi-label lesion annotation, called AER-GCN. The KG-augmented model employs GCN to learn the explicit label relations in external medical KG, and aggregates the explicit relations into statistical graph built from label co-occurrence information. Specially, we present three approaches on modeling the explicit label correlations in external knowledge, and two approaches on incorporating the explicit relations into co-occurrence relations for lesion annotation. We exploit SNOMED CT as the source of external knowledge and evaluate the performance of AER-GCN on the ChestX-ray and IU X-ray datasets. Extensive experiments demonstrate that our model outperforms other state-of-the-art models.