The multi-label text classification task aims to tag a document with a series of labels. Previous studies usually treated labels as symbols without semantics and ignored the relation among labels, which caused information loss. In this paper, we show that explicitly modeling label semantics can improve multilabel text classification. We propose a hybrid neural network model to simultaneously take advantage of both label semantics and fine-grained text information. Specifically, we utilize the pre-trained BERT model to compute context-aware representation of documents. Furthermore, we incorporate the label semantics in two stages. First, a novel label graph construction approach is proposed to capture the label structures and correlations. Second, we propose a neoteric attention mechanism-adjustive attention to establish the semantic connections between labels and words and to obtain the label-specific word representation. The hybrid representation that combines context-aware feature and label-special word feature is fed into a document encoder to classify. Experimental results on two publicly available datasets show that our model is superior to other state-of-the-art classification methods.

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<abstract> <p>Electronic Medical Record (EMR) is the data basis of intelligent diagnosis. The diagnosis results of an EMR are multi-disease, including normal diagnosis, pathological diagnosis and complications, so intelligent diagnosis can be treated as multi-label classification problem. The distribution of diagnostic results in EMRs is imbalanced. And the diagnostic results in one EMR have a high coupling degree. The traditional rebalancing methods does not function effectively on highly coupled imbalanced datasets. This paper proposes Double Decoupled Network (DDN) based intelligent diagnosis model, which decouples representation learning and classifier learning. In the representation learning stage, Convolutional Neural Networks (CNN) is used to learn the original features of the data. In the classifier learning stage, a Decoupled and Rebalancing highly Imbalanced Labels (DRIL) algorithm is proposed to decouple the highly coupled diagnostic results and rebalance the datasets, and then the balanced datasets is used to train the classifier. This paper evaluates the proposed DDN using Chinese Obstetric EMR (COEMR) datasets, and verifies the effectiveness and universality of the model on two benchmark multi-label text classification datasets: Arxiv Academic Papers Datasets (AAPD) and Reuters Corpus1 (RCV1). Demonstrating the effectiveness of the proposed methods is an imbalanced obstetric EMRs. The accuracy of DDN model on COEMR, AAPD and RCV1 datasets is 84.17, 86.35 and 93.87% respectively, which is higher than the current optimal experimental results.</p> </abstract>

Purpose: Ocular blood flow (OBF) is an important risk factor for incidence, prevalence and progression of some ocular disorders. To date, there are very limited therapeutic options to increase OBF. This study investigated the effect of dobutamine on OBF of heathy adults using 3D pseudocontinuous arterial spin labelling (3D-pcASL), and explored the risk factors associated with OBF.Methods: Forty-three healthy participants (86 eyes) were given an intravenous injection of dobutamine. We measured OBF using 3D-pcASL with a 3.0T- MRI scanner, OBF values were independently obtained by two doctors from the OBF map. We also collected physiological parameters using a vital signs monitor. The OBF and physiological parameters in the in the period before and after dobutamine injection states were obtained.Results: OBF increased significantly after dobutamine injection using paired t test method (from 22.43 ± 9.87 to 47.73 ± 14.02 ml/min/100g, p &lt; 0.001). Age, heart rate and systolic blood pressure were the main risk factors affecting OBF using logistic regression analysis (all p values &lt; 0.05).Conclusion: To the best of our knowledge, this is the first study observing the effect of dobutamine on OBF. Our findings indicated that intravenously injected dobutamine increased OBF, making it a possible option to counteract ocular vascular ischaemia in the future.

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