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Abstract It is challenging to locate small-airway obstructions induced by chronic obstructive pulmonary disease (COPD) directly from visualization using available medical imaging techniques. Accordingly, this study proposes an innovative and noninvasive diagnostic method to detect obstruction locations using computational fluid dynamics (CFD) and convolutional neural network (CNN). Specifically, expiratory airflow velocity contours were obtained from CFD simulations in a subject-specific 3D tracheobronchial tree. One case representing normal airways and 990 cases associated with different obstruction sites were investigated using CFD. The expiratory airflow velocity contours at a selected cross section in the trachea were labeled and stored as the database for training and testing two CNN models, i.e., ResNet50 and YOLOv4. Gradient-weighted class activation mapping (Grad-CAM) and the Pearson correlation coefficient were employed and calculated to classify small-airway obstruction locations and pulmonary airflow pattern shifts and highlight the highly correlated regions in the contours for locating the obstruction sites. Results indicate that the airflow velocity pattern shifts are difficult to directly visualize based on the comparisons of CFD velocity contours. CNN results show strong relevance exists between the locations of the obstruction and the expiratory airflow velocity contours. The two CNN-based models are both capable of classifying the left lung, right lung, and both lungs obstructions well using the CFD simulated airflow contour images with total accuracy higher than 95.07%. The two automatic classification algorithms are highly transformative to clinical practice for early diagnosis of obstruction locations in the lung using the expiratory airflow velocity distributions, which could be imaged using hyperpolarized magnetic resonance imaging. 

Prescription (aka Rx) drugs can be easily overprescribed and lead to drug abuse or opioid overdose. Accordingly, a state-run prescription drug monitoring program (PDMP) in the United States has been developed to reduce overprescribing. However, PDMP has limited capability in detecting patients' potential overprescribing behaviors, impairing its effectiveness in preventing drug abuse and overdose in patients. In this paper, we propose a novel model RxNet, which builds 1) a dynamic heterogeneous graph to model Rx refills that are essentially prescribing and dispensing (P&D) relationships among various patients, 2) an RxLSTM network to explore the dynamic Rx-refill behavior and medical condition variation of patients, and 3) a dosing-adaptive network to extract and recalibrate dosing patterns and obtain the refined patient representations which are finally utilized for overprescribing detection. The extensive experimental results on a one-year state-wide PDMP data demonstrate that RxNet consistently outperforms state-of-the-art methods in predicting patients at high risk of opioid overdose and drug abuse.

 

Heterogeneous Graph Neural Networks (HGNNs) have drawn increasing attention in recent years and achieved outstanding performance in many tasks. The success of the existing HGNNs relies on one fundamental assumption, i.e., the original heterogeneous graph structure is reliable. However, this assumption is usually unrealistic, since the heterogeneous graph in reality is inevitably noisy or incomplete. Therefore, it is vital to learn the heterogeneous graph structure for HGNNs rather than rely only on the raw graph structure. In light of this, we make the first attempt towards learning an optimal heterogeneous graph structure for HGNNs and propose a novel framework HGSL, which jointly performs Heterogeneous Graph Structure Learning and GNN parameters learning for classification task. Different from traditional GSL on homogeneous graph, considering the heterogeneity of different relations in heterogeneous graph, HGSL generates each relation subgraph independently. Specifically, in each generated relation subgraph, HGSL not only considers the feature similarity by generating feature similarity graph, but also considers the complex heterogeneous interactions in features and semantics by generating feature propagation graph and semantic graph. Then, these graphs are fused to a learned heterogeneous graph and optimized together with a GNN towards classification objective. Extensive experiments on real-world graphs demonstrate that the proposed framework significantly outperforms the state-of-the-art methods.