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SSRN 2023年

作者： Han, Pu Ye, Dongyu Gu, Liang Chen, Wenqi Wei, Hongchen Li, Xiaoyan School of Management Nanjing University of Posts & Telecommunications Nanjing210003 China Jiangsu Provincial Key Laboratory of Data Engineering and Knowledge Service Nanjing210023 China School of Basic Medical Sciences Nanjing Medical University Nanjing210029 China

Accurate analysis of patients' feedback on various medical aspects is of great importance for improving the quality of healthcare services. In this paper, we address the task of extracting aspects and opinions by adopting a generative end-to-end deep learning model. And we address the challenge of limited high-quality labeled data in aspect-based sentiment analysis by introducing a data augmentation method. To evaluate the effectiveness of our proposed generative model and data augmentation technique in the healthcare domain, we collected reviews related to diagnosis and treatment from online health communities(OHC) and verified the effectiveness of the proposed model. The experimental results demonstrate the superiority of generative models in complex aspect-based sentiment analysis tasks. Furthermore, we find that our data augmentation method is effective in enhancing the experimental outcomes. Our work contributes to the advancement of natural language processing techniques in the healthcare domain, and it has the potential to improve the quality of healthcare services through more fine-grained analysis of patients' feedback. © 2023, The Authors. All rights reserved.

关键词： Sentiment analysis

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Generative Aspect Based Sentiment Analysis for Online Health Community with data Augmentation

SSRN

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SSRN 2023年

Accurate analysis of patients' feedback on various medical aspects is of great importance for improving the quality of healthcare services. In this paper, we address the task of extracting aspects and opinions by adopting a generative end-to-end deep learning model. And we address the challenge of limited high-quality labeled data in aspect-based sentiment analysis by introducing a data augmentation method. To evaluate the effectiveness of our proposed generative model and data augmentation technique in the healthcare domain, we collected reviews related to diagnosis and treatment from online health communities(OHC) and verified the effectiveness of the proposed model. The experimental results demonstrate the superiority of generative models in complex aspect-based sentiment analysis tasks, showing a noteworthy nearly 13% increase in F1 score. Furthermore, we find that our data augmentation method is effective in enhancing the experimental outcomes. Our work contributes to the advancement of natural language processing techniques in the healthcare domain, and it has the potential to improve the quality of healthcare services through more fine-grained analysis of patients' feedback. Our code and dataset are available at https://***/woloc/OHC_*** now. © 2023, The Authors. All rights reserved.

关键词： Sentiment analysis

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Learning Group-Disentangled Representation for Interpretable Thoracic Pathologic Prediction

Learning Group-Disentangled Representation for Interpretable...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hao Li Yirui Wu Hexuan Hu Hu Lu Yong Lai Shaohua Wan Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University College of Computer and Information Hohai University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University School of Computer Science and Communication Engineering Jiangsu University Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Deep learning methods have shown significant performance in medical image analysis tasks. However, they generally act like ”black box” without explanations in both feature extraction and decision processes, leading to lack of clinical insights and high risk assessments. To aid deep learning in envisioning diseases with visual clues, we propose Representation Group-Disentangling Network (RGD-Net), which can completely disentangle feature space of input X-ray images into several independent feature groups, each corresponding to a specific disease. Taking several semantically related and labeled X-ray images as input, RGD-Net firstly extracts completely group-disentangled representations of diseases through Group-Disentangle Module, which applies group-swap and linking operations to construct latent space by enforcing semantic consistency of attributes. To prevent learning degenerate representations defined as shortcut problem, we further introduce adversarial constricts on mapping from features to diseases, thus avoiding model collapse with former free-form disentanglement. Experiments on chestxray-14 and ChestXpert datasets demonstrate that RGD-Net are effective in predicting diseases with remarkable advantages, which leverage potential factors contributing to different diseases, thus enhancing interpretability in working patterns of deep learning methods.

关键词： Deep learning Training Visualization Image analysis Semantics Feature extraction Risk management

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Soft Film Plate Method Based Ultrafine Ag-Cu Nanosheets with Multi-Nanozyme Activities for Neurotransmitter Biosensing

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SSRN 2023年

作者： Kong, Lijun Fan, Lin Liu, Hao Zhang, JiaWei Hu, Mengdi Fan, Li Zhu, Hongliang Yan, Shancheng School of Geographic and Biologic Information Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing210023 China State Key Laboratory of Bioelectronics Jiangsu Key Laboratory for Biomaterials and Devices Southeast University Nanjing210096 China Nanjing University Nanjing210093 China

Ag and Cu based nanostructures serve as advanced functional materials for biomedical applications, due to their unique properties. Here, we proposed a novel Neurotransmitter Biosensing method based on Ag-Cu composite nanozyme, synthesized through the soft film plate method. Supported by the soft film template, the Ag-Cu nanozymes were stably kept to an ultrafine 2D structure with high monodispersity, which provided a large specific surface area and sufficient binding sites, leading to controllable and improved multi-nanozyme activities over similar-sized mono-Ag and mono-Cu, and up to 4.95 times of natural enzyme-level. Beyond that, the multi-path enzymatic reaction processes catalyzed by Ag-Cu composite nanozymes were firstly theoretically discussed in detail, according to the theoretical redox potential of redox couples in the reaction systems. On this basis, the Ag-Cu nanosheets based Neurotransmitter Biosensing is successfully applied in rapid detection for glutathione and dopamine, possessing a linear range of 10~100 μM and 1~10 μM, and a detection limit of 5.066 μM and 0.288 μM, respectively, which exhibited superior performance for biomedical purposes over most commercially available products in speed and precision. © 2023, The Authors. All rights reserved.

关键词： Binary alloys

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Soft Film Plate Method Based Ultrafine Ag-Cu Nanosheets with Multi-Nanozyme Activities for Neurotransmitter Biosensing

SSRN

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SSRN 2023年

作者： Fan, Lin Kong, Lijun Liu, Hao Zhang, JiaWei Hu, Mengdi Fan, Li Zhu, Hongliang Yan, Shancheng School of Geographic and Biologic Information Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing210023 China Nanjing University Nanjing210093 China State Key Laboratory of Bioelectronics Jiangsu Key Laboratory for Biomaterials and Devices Southeast University Nanjing210096 China

Ag and Cu based nanostructures serve as advanced functional materials for biomedical applications, due to their unique properties. Here, we proposed a novel neurotransmitter biosensing method based on Ag-Cu composite nanozyme, synthesized through the soft film plate method. Supported by the soft film template, the Ag-Cu nanozymes were stably kept to an ultrafine 2D structure with high monodispersity, which provided a large specific surface area and sufficient binding sites, leading to controllable and improved multi-nanozyme activities over similar-sized mono-Ag and mono-Cu, and up to 4.95 times of natural enzyme-level. The multi-path enzymatic reaction processes catalyzed by Ag-Cu composite nanozymes were firstly theoretically discussed in detail, according to the theoretical redox potential of redox couples in the reaction systems. On this basis, the Ag-Cu nanosheets based neurotransmitter biosensing is successfully applied in rapid detection for glutathione and dopamine, possessing a linear range of 10~100 μM and 1~10 μM, and a detection limit of 3.01 μM and 0.29 μM, respectively, which exhibited superior performance for biomedical purposes over most commercially available products in speed and precision. © 2023, The Authors. All rights reserved.

关键词： Binary alloys

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LRCu: A High-Performance Parallel Program for Identifying Hub Genes

LRCu: A High-Performance Parallel Program for Identifying Hu...

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International Congress on Image and Signal Processing, BioMedical engineering and Informatics (CISP-BMEI)

作者： Jiaxin Liu Xinglai Ji Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunan Yunnan University Kunming China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

The LeaderRank algorithm identifies influential nodes using the idea of the random walk and performs well in the identification of hub genes in biological networks. The rapid development of biological technologies promotes the evolution of networks to be highly complex and increasing largely in size. In this study, we introduce LRCu, for computing LeaderRank using CUDA, to identify hub genes. The results show that the parallel implementation is highly effective and achieves a speedup of more than 42 times for a 13,000-gene network, thereby enhancing large-scale network computation.

关键词： Evolution (biology) Signal processing algorithms Graphics processing units Signal processing Informatics Biomedical engineering

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LSBert: Lexical Simplification Based on BERT

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IEEE/ACM Transactions on Audio Speech and Language Processing 2021年 29卷 3064-3076页

作者： Qiang, Jipeng Li, Yun Zhu, Yi Yuan, Yunhao Shi, Yang Wu, Xindong Department of Computer Science Jiangsu Yangzhou China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Anhui Hefei China Mininglamp Academy of Sciences Minininglamp Beijing 100864 China

Lexical simplification (LS) aims at replacing complex words with simpler alternatives. LS commonly consists of three main steps: complex word identification, substitute generation, and substitute ranking. Existing LS methods focus on the contextual information of the complex word in the last step (substitute ranking). However, they miss out the following two facts: (1) The word complexity of a polysemous word is very closely related to its context;(2) The step of substitute generation regardless of the context will inevitably produce a large number of spurious candidates. Therefore, we propose a novel LS system LSBert based on pretrained language model BERT to address the aforementioned issues, which is capable of making use of the wider context when both identifying the words in need of simplification and generating substitute candidates for the complex words. Specifically, LSBert consists of a network for complex word identification by fine-tuning BERT and a network for substitute generation based on BERT. Experimental results show that LSBert performs well in both complex word identification and substitute generation, achieving state-of-the-art results in three benchmarks. © 2014 IEEE.

关键词： BERT Complex word identification Lexical simplification Sentence simplification

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Local and global structure-aware entropy regularized mean teacher model for 3d left atrium segmentation 23rd

Local and global structure-aware entropy regularized mean te...

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23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Hang, Wenlong Feng, Wei Liang, Shuang Yu, Lequan Wang, Qiong Choi, Kup-Sze Qin, Jing School of Computer Science and Technology Nanjing Tech University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Department of Radiation Oncology Stanford University StanfordCA94305 United States CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China School of Nursing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9783030597092

Emerging self-ensembling methods have achieved promising semi-supervised segmentation performances on medical images through forcing consistent predictions of unannotated data under different perturbations. However, the consistency only penalizes on independent pixel-level predictions, making structure-level information of predictions not exploited in the learning procedure. In view of this, we propose a novel structure-aware entropy regularized mean teacher model to address the above limitation. Specifically, we firstly introduce the entropy minimization principle to the student network, thereby adjusting itself to produce high-confident predictions of unannotated images. Based on this, we design a local structural consistency loss to encourage the consistency of inter-voxel similarities within the same local region of predictions from teacher and student networks. To further capture local structural dependencies, we enforce the global structural consistency by matching the weighted self-information maps between two networks. In this way, our model can minimize the prediction uncertainty of unannotated images, and more importantly that it can capture local and global structural information and their complementarity. We evaluate the proposed method on a publicly available 3D left atrium MR image dataset. Experimental results demonstrate that our method achieves outstanding segmentation performances than the state-of-the-art approaches in scenes with limited annotated images. © Springer Nature Switzerland AG 2020.

关键词： Forecasting

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Threshold distribution of equal states for quantitative amplitude fluctuations

arXiv

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arXiv 2023年

作者： Yao, Wenpo Yao, Wenli Wang, Jun School of Geographic and Biologic Information Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing210023 China Ministry of Education State Key Laboratory of Hydroscience and Engineering Tsinghua University Beijing100084 China

Objective. The distribution of equal states (DES) quantifies amplitude fluctuations in biomedical signals. However, under certain conditions, such as a high resolution of data collection or special signal processing techniques, equal states may be very rare, whereupon the DES fails to measure the amplitude fluctuations. Approach. To address this problem, we develop a novel threshold DES (tDES) that measures the distribution of differential states within a threshold. To evaluate the proposed tDES, we first analyze five sets of synthetic signals generated in different frequency bands. We then analyze sleep electroencephalography (EEG) datasets taken from the public PhysioNet. Main results. Synthetic signals and detrend-filtered sleep EEGs have no neighboring equal values;however, tDES can effectively measure the amplitude fluctuations within these data. The tDES of EEG data increases significantly as the sleep stage increases, even with datasets covering very short periods, indicating decreased amplitude fluctuations in sleep EEGs. Generally speaking, the presence of more low-frequency components in a physiological series reflects smaller amplitude fluctuations and larger DES. *** tDES provides a reliable computing method for quantifying amplitude fluctuations, exhibiting the characteristics of conceptual simplicity and computational robustness. Our findings broaden the application of quantitative amplitude fluctuations and contribute to the classification of sleep stages based on EEG data. © 2023, CC0.

关键词： Electroencephalography

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Pathological brain detection based on online sequential extreme learning machine

Pathological brain detection based on online sequential extr...

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IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Siyuan Lu Hainan Wang Xueyan Wu Shuihua Wang School of Computer Science and Technology Nanjing Normal University Nanjing Jiangsu China State Key Lab of CAD & CG Zhejiang University Hangzhou Zhejiang China Key laboratory of symbolic computation and knowledge engineering of ministry of education Jilin University Changchun Jilin China Key Laboratory of Statistical information technology and data mining State Statistics Bureau Chengdu Sichuan China Department of Electrical Engineering CUNY New York NY USA

ISBN: (纸本)9781509034857

Magnetic resonance imaging (MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography (CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies (WE) were extracted from each brain MR image to form the feature vector. Then, an online sequential extreme learning machine (OS-ELM) was trained to differentiate pathological brains from the healthy controls. The experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%, which suggested that our method is effective.

关键词： Entropy Discrete wavelet transforms Feature extraction Pathology Training Magnetic resonance imaging Diseases

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