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Identification of Cognitive Dysfunction in Patients with T2DM Using Whole Brain Functional Connectivity

Genomics, Proteomics & Bioinformatics 2019年第4期17卷 441-452页

作者： Zhenyu Liu Jiangang Liu Huijuan Yuan Taiyuan Liu Xingwei Cui Zhenchao Tang Yang Du Meiyun Wang Yusong Lin Jie Tian CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China Department of Endocrinology and Metabolism Henan Provincial People’s Hospital&the People’s Hospital of Zhengzhou UniversityZhengzhou 450003China Department of Radiology Henan Provincial People’s Hospital&the People’s Hospital of Zhengzhou UniversityZhengzhou 450003China Cooperative Innovation Center for Internet Healthcare&School of Software Zhengzhou UniversityZhengzhou 450003China School of Mechanical Electrical&Information EngineeringShandong University(Weihai)Weihai 264209China University of Chinese Academy of Sciences Beijing 100080China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of MedicineBeihang UniversityBeijing 100191China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi’an 710126China

Majority of type 2 diabetes mellitus(T2DM)patients are highly susceptible to several forms of cognitive impairments,particularly ***,the underlying neural mechanism of these cognitive impairments remains *** aimed to investigate the correlation between whole brain resting state functional connections(RSFCs)and the cognitive status in 95 patients with *** constructed an elastic net model to estimate the Montreal Cognitive Assessment(MoCA)scores,which served as an index of the cognitive status of the patients,and to select the RSFCs for further ***,we utilized a machine learning technique to evaluate the discriminative ability of the connectivity pattern associated with the selected *** estimated and chronological MoCA scores were significantly correlated with R=0.81 and the mean absolute error(MAE)=***,cognitive impairments of patients with T2DM can be identified using the RSFC pattern with classification accuracy of 90.54%and the area under the receiver operating characteristic(ROC)curve(AUC)of *** connectivity pattern not only included the connections between regions within the default mode network(DMN),but also the functional connectivity between the task-positive networks and the DMN,as well as those within the task-positive *** results suggest that an RSFC pattern could be regarded as a potential biomarker to identify the cognitive status of patients with T2DM.

关键词： Type 2 diabetes mellitus Resting state functional connectivity Elastic net Support vector machines MoCA

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Automatic Extraction of Medication Mentions from Tweets-Overview of the BioCreative VII Shared Task 3 Competition

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database : the journal of biological databases and curation 2023年第2023期2023卷 baac108页

作者： Davy Weissenbacher Karen O'Connor Siddharth Rawal Yu Zhang Richard Tzong-Han Tsai Timothy Miller Dongfang Xu Carol Anderson Bo Liu Qing Han Jinfeng Zhang Igor Kulev Berkay Köprü Raul Rodriguez-Esteban Elif Ozkirimli Ammer Ayach Roland Roller Stephen Piccolo Peijin Han V G Vinod Vydiswaran Ramya Tekumalla Juan M Banda Parsa Bagherzadeh Sabine Bergler João F Silva Tiago Almeida Paloma Martinez Renzo Rivera-Zavala Chen-Kai Wang Hong-Jie Dai Luis Alberto Robles Hernandez Graciela Gonzalez-Hernandez Department of Computational Biomedicine Cedars-Sinai Medical Center Los Angeles CA USA. DBEI The Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Rd Zhongli District Taoyuan 320 Taiwan. IoX Center National Taiwan University Da'an District Section 4 Roosevelt Rd No. 1 Barry Lam Hall Taipei 106 Taiwan. Research Center for Humanities and Social Sciences Academia Sinica No. 128 Section 2 Academia Rd Nangang District Taipei 115 Taiwan. Computational Health Informatics Program Boston Children's Hospital Boston MA USA. Department of Pediatrics Harvard Medical School Boston MA USA. NVIDIA Santa Clara CA USA. Department of Statistics Florida State University Tallahassee FL USA. Data and Analytics Chapter F. Hoffmann-La Roche Ltd Switzerland. Pharmaceutical Research and Early Development Roche Innovation Center Basel Switzerland. Speech and Language Technology Lab DFKI Berlin Germany. Department of Biology Brigham Young University Provo UT USA. Department of Computational Medicine and Bioinformatics Medical School University of Michigan Ann Arbor MI USA. Department of Learning Health Sciences Medical School University of Michigan Ann Arbor MI USA. School of Information University of Michigan Ann Arbor MI USA. Department of Computer Science Georgia State University Atlanta GA USA. CLaC Labs Concordia University Montreal Canada. DETI Institute of Electronics and Informatics Engineering of Aveiro University of Aveiro Portugal. Department of Computation University of A Coruña Spain. Computer Science and Engineering Department Universidad Carlos III de Madrid Madrid Spain. Big Data Laboratory Chunghwa Telecom Laboratories Taoyuan Taiwan. Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan. Department of Electrical Engineering College of Electrical Engineering and Computer Sci

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user's publicly available tweets (the user's 'timeline'). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding those tweets in a user's timeline that mention specific health-related concepts such as medications requires addressing extreme imbalance. Task 3 called for detecting tweets in a user's timeline that mentions a medication name and, for each detected mention, extracting its span. The organizers made available a corpus consisting of 182 049 tweets publicly posted by 212 Twitter users with all medication mentions manually annotated. The corpus exhibits the natural distribution of positive tweets, with only 442 tweets (0.2%) mentioning a medication. This task was an opportunity for participants to evaluate methods that are robust to class imbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. This study summarizes the corpus created by the organizers and the approaches taken by the participating teams for this challenge. The corpus is freely available at https://***/tasks/biocreative-vii/track-3/. The methods and the results of the competing systems are analyzed with a focus on the approaches taken for learning from class-imbalanced data.

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ClsGAN: Selective attribute editing based on classification adversarial network

arXiv

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arXiv 2019年

作者： Liu, Ying Fan, Heng Ni, Fuchuan Xiang, Jinhai College of Informatics Huazhong Agricultural University Wuhan430070 China Hubei Engineering Technology Research Center of Agricultural Big Data Huazhong Agricultural University Wuhan430070 China Department of Computer & Information Sciences Temple University Philadelphia19122 United States

Attribution editing has shown remarking progress by the incorporating of encoder-decoder structure and generative adversarial network. However, there are still some challenges in the quality and attribute transformation of the generated images. Encoder-decoder structure leads to blurring of images and the skip-connection of encoder-decoder structure weakens the attribute transfer ability. To address these limitations, we propose a classification adversarial model(Cls-GAN) that can balance between attribute transfer and generated photo-realistic images. Considering that the transfer images are affected by the original attribute using skip-connection, we introduce upper convolution residual network(Tr-resnet) to selectively extract information from the source image and target label. Specially, we apply to the attribute classification adversarial network to learn about the defects of attribute transfer images so as to guide the generator. Finally, to meet the requirement of multimodal and improve reconstruction effect, we build two encoders including the content and style network, and select a attribute label approximation between source label and the output of style network. Experiments that operates at the dataset of CelebA show that images are superiority against the existing state-of-the-art models in image quality and transfer accuracy. Experiments on wikiart and seasonal datasets demonstrate that ClsGAN can effectively implement styel transfer. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

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DPHL:A DIA Pan-human Protein Mass Spectrometry Library for Robust Biomarker Discovery

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Genomics, Proteomics & Bioinformatics 2020年第2期18卷 104-119页

作者： Tiansheng Zhu Yi Zhu Yue Xuan Huanhuan Gao Xue Cai Sander R.Piersma Thang V.Pham Tim Schelfhorst Richard R.G.D.Haas Irene V.Bijnsdorp Rui Sun Liang Yue Guan Ruan Qiushi Zhang Mo Hu Yue Zhou Winan J.Van Houdt Tessa Y.S.Le Large Jacqueline Cloos Anna Wojtuszkiewicz Danijela Koppers-Lalic Franziska Bottger Chantal Scheepbouwer Ruud H.Brakenhoff Geert J.L.H.van Leenders Jan N.M.Ijzermans John W.M.Martens Renske D.M.Steenbergen Nicole C.Grieken Sathiyamoorthy Selvarajan Sangeeta Mantoo Sze S.Lee Serene J.Y.Yeow Syed M.F.Alkaff Nan Xiang Yaoting Sun Xiao Yi Shaozheng Dai Wei Liu Tian Lu Zhicheng Wu Xiao Liang Man Wang Yingkuan Shao Xi Zheng Kailun Xu Qin Yang Yifan Meng Cong Lu Jiang Zhu Jin'e Zheng Bo Wang Sai Lou Yibei Dai Chao Xu Chenhuan Yu Huazhong Ying Tony K.Lim Jianmin Wu Xiaofei Gao Zhongzhi Luan Xiaodong Teng Peng Wu Shi'ang Huang Zhihua Tao Narayanan G.Iyer Shuigeng Zhou Wenguang Shao Henry Lam Ding Ma Jiafu Ji Oi L.Kon Shu Zheng Ruedi Aebersold Connie R.Jimenez Tiannan Guo Zhejiang Provincial Laboratory of Life Sciences and Biomedicine Westlake UniversityHangzhou 310024China Key Laboratory of Structural Biology of Zhejiang Province School of Life SciencesWestlake UniversityHangzhou 310024China Institute of Basic Medical Sciences Westlake Institute for Advanced StudyHangzhou 310024China School of Computer Science Shanghai Key Laboratory of Intelligent Information ProcessingFudan UniversityShanghai 200433China Thermo Fisher Scientific(BREMEN)GmbH Bremen 28195Germany OncoProteomics Laboratory Department of Medical OncologyVU University Medical CenterVU UniversityAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamUrologyCancer Center AmsterdamAmsterdam 1011The Netherlands Thermo Fisher Scientific Shanghai 201206China The Netherlands Cancer Institute Surgical OncologyAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamSurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamPediatric Oncology/HematologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamHematologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamMedical OncologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamNeurosurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamPathologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamOtolaryngology/Head and Neck SurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Erasmus MC University Medical Center PathologyRotterdam 1016LVThe Netherlands Erasmus MC University Medical Center SurgeryRotterdam 1016LVThe Netherlands Erasmus MC University Medical Center Medical OncologyRotterdam 1016LVThe Netherlands Department of Anatomical Pathology Singapore General HospitalSingapore 169608Singapore Division of Medical Scie

To address the increasing need for detecting and validating protein biomarkers in clinical specimens,mass spectrometry(MS)-based targeted proteomic techniques,including the selected reaction monitoring(SRM),parallel reaction monitoring(PRM),and massively parallel dataindependent acquisition(DIA),have been *** optimal performance,they require the fragment ion spectra of targeted peptides as prior *** this report,we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue *** build the spectral resource,we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on *** then applied the workflow to generate DPHL,a comprehensive DIA pan-human library,from 1096 data-dependent acquisition(DDA)MS raw files for 16 types of cancer *** extensive spectral resource was then applied to a proteomic study of 17 prostate cancer(PCa)***,PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was *** a second application,the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma(DLBCL)patients and 18 healthy control *** expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by *** data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker *** is freely accessible at https://***/page/***?id=IPX0001400000.

关键词： data-independent acquisition Parallel reaction monitoring Spectral library Prostate cancer Diffuse large B cell lymphoma

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Deep Learning Algorithms-based Object Detection and Localization Revisited

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Journal of Physics: Conference Series 2021年第1期1892卷

作者： Safa Riyadh Waheed Norhaida Mohd Suaib Mohd Shafry Mohd Rahim Myasar Mundher Adnan A. A. Salim Big Data Center & Faculty of Engineering School of Computing Universiti Teknologi Malaysia 81310 Johor Bahru Malaysia Computer Techniques Engineering Department Faculty of Information Technology Imam Jaafar Al-Sadiq University Baghdad Iraq UTM-IRDA Digital Media Institute of Human-Centred Engineering Universiti Teknologi Malaysia 81310 Johor Bahru Malaysia Islamic University Najaf Iraq Laser Center & Physics Department Faculty of Science Universiti Teknologi Malaysia 81310 Johor Bahru Malaysia

The computer vision (CV) is an emerging area with sundry promises. This communication encompasses the past development, recent trends and future directions of the CV in the context of deep learning (DL) algorithms-based object detections and localizations techniques. To identify the object location inside an image and recognize it by a computer program as fast as the human brain the machine learning and DL techniques have been evolved. However, the main limitations of the machine are related to the prolonged time consumption to handle vast amount of data to perform the same task as the human brain. To overcome these shortcomings, the convolution neural networks (NNs)-based deep NN has been developed, which detects and classifies the object with high precision. To train the deep NNs, massive amount of data (in the form of images and videos) and time is needed, making the computational cost of the CV very high. Thus, transfer learning techniques have been proposed wherein a model trained on one task can be reused on another linked task, thereby producing excellent outcomes. In this spirit, diverse DL-based algorithms have been introduced to detect and classify the object. These algorithms include the region-based convolutional NN (R-CNN), fast R-CNN, Faster R-CNN, mask E-CNN and You Only Look Once. A comparative evaluation among these techniques has been made to reveal their merits and demerits in the CV.

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A robust black carbon prediction model derived from observational datasets in the Yangtze River Delta region, China

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Environmental Pollution 2025年 377卷 126361页

作者： Duan, Lian Fung, Pak Lun Fu, Qingyan Chen, Jia Huo, Juntao Huang, Kan Wang, Guochen Zaidan, Martha Arbayani Guo, Zhigang Hussein, Tareq Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention Department of Environmental Science & Engineering Fudan University Shanghai200438 China Faculty of Science University of Helsinki UHEL PL 64 HelsinkiFI-00014 Finland Shanghai Academy of Environmental Sciences Shanghai200233 China Shanghai Environmental Monitoring Center Shanghai200030 China Department of Physics School of Science University of Jordan Amman11942 Jordan Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province Department of Atmospheric Sciences School of Earth Sciences Zhejiang University Hangzhou310027 China Department of Computer Science Faculty of Science University of Helsinki UHEL PL 64 HelsinkiFI-00014 Finland Shanghai200062 China IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health Fudan University Shanghai200433 China Observation and Research Station of Huaniaoshan East China Sea Ocean-Atmosphere Integrated Ecosystem Ministry of Natural Resources China

Black carbon (BC) is a short-lived pollutant with significant environment and human health impacts. Monitoring BC is important, but its spatial coverage is limited. Therefore, predicting BC concentration is crucial in densely populated regions like the Yangtze River Delta (YRD). This study explores the machine learning (ML) models, including IAP, LASSO, RF, and SNN, to develop a robust BC prediction model for the YRD, based on BC behavior at the Dianshan Lake (DSL) site. The annual BC concentration at DSL was 1.37 μg/m3, mainly from liquid fuel combustion (68 %), biomass burning (16 %), and coal combustion contributing (16 %). The ML model was first trained with DSL data (June 2021–June 2022) and then applied to other YRD sites in four scenarios based on air mass trajectories. The SNN model had R2 values over 0.80 for all scenarios at the DSL site. The model performed better during peak traffic periods, highlighting traffic's impact on BC levels. These models were applied to the YRD sites with R2 from 0.45 to 0.84. Hangzhou (HZ, 0.75–0.81), Nanjing (NJ, 0.78–0.80), and Pudong (PD, 0.76–0.84) showed the highest results. This suggests the model effectively captures BC concentrations from sources with similar emissions and meteorological conditions. © 2025 Elsevier Ltd

关键词： River pollution

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Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends

MedComm

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MedComm 2025年第6期6卷 e70247页

作者： Ruoyun Wang Fanxuan Chen Haoman Chen Chenxing Lin Jincen Shuai Yutong Wu Lixiang Ma Xiaoqu Hu Min Wu Jin Wang Qi Zhao Jianwei Shuai Jingye Pan Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine Vision and Brain Health) Wenzhou Institute University of Chinese Academy of Sciences Wenzhou China. Wenzhou Medical University Wenzhou China. UCSC Baskin School of Engineering University of California Santa Cruz California USA. Department of Anatomy Histology & Embryology School of Basic Medical Sciences Fudan University Shanghai China. Department of Medicine Harvard Medical School and Brigham and Women's Hospital Boston Massachusetts USA. Joint Research Centre on Medicine The Affiliated Xiangshan Hospital of Wenzhou Medical University Ningbo China. Stony Brook University Stony Brook New York USA. School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China. Key Laboratory of Intelligent Treatment and Life Support for Critical Diseases of Zhejiang Province Wenzhou China. Department of Big Data in Health Science The First Affiliated Hospital of Wenzhou Medical University Wenzhou China. Zhejiang Engineering Research Center for Hospital Emergency and Process Digitization Wenzhou China.

The high-resolution three-dimensional (3D) images generated with digital breast tomosynthesis (DBT) in the screening of breast cancer offer new possibilities for early disease diagnosis. Early detection is especially important as the incidence of breast cancer increases. However, DBT also presents challenges in terms of poorer results for dense breasts, increased false positive rates, slightly higher radiation doses, and increased reading times. Deep learning (DL) has been shown to effectively increase the processing efficiency and diagnostic accuracy of DBT images. This article reviews the application and outlook of DL in DBT-based breast cancer screening. First, the fundamentals and challenges of DBT technology are introduced. The applications of DL in DBT are then grouped into three categories: diagnostic classification of breast diseases, lesion segmentation and detection, and medical image generation. Additionally, the current public databases for mammography are summarized in detail. Finally, this paper analyzes the main challenges in the application of DL techniques in DBT, such as the lack of public datasets and model training issues, and proposes possible directions for future research, including large language models, multisource domain transfer, and data augmentation, to encourage innovative applications of DL in medical imaging.

关键词： deep learning digital breast tomosynthesis early breast cancer screening medical image analysis public database

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Integration of droplet microfluidic tools for single-cell functional metagenomics: an engineering head start

TechRxiv

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TechRxiv 2021年

作者： Conchouso, David Al-Ma’abadi, Amani Behzad, Hayedeh Alarawi, Mohammed Hosokawa, Masahito Nishikawa, Yohei Takeyama, Haruko Mineta, Katsuhiko Gojobori, Takashi Departamento de Ingeniería Industrial y Mecánica Escuela de Ingeniería Universidad de las Américas Puebla Puebla72810 Mexico Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia Biological and Environmental Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia Research Organization for Nano & Life Innovation Waseda University Tokyo162–0041 Japan Department of Life Science and Medical Bioscience Waseda University Tokyo162–8480 Japan Institute for Advanced Research of Biosystem Dynamics Waseda Research Institute for Science and Engineering Waseda University Tokyo169-8555 Japan Computational Bio Big-Data Open Innovation Laboratory AIST-Waseda University Tokyo169–0072 Japan Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia

Droplet microfluidics techniques have shown promising results to study single-cells at high throughput. However, their adoption in laboratories studying "-omics" sciences is still irrelevant because of the field’s complex and multidisciplinary nature. To facilitate their use, here we provide engineering details and organized protocols for integrating three droplet-based microfluidic technologies into the metagenomic pipeline to enable functional screening of bioproducts at high throughput. First, a device encapsulating single-cells in droplets at a rate of ~ 250 Hz is described considering droplet size and cell growth. Then, we expand on previously reported fluorescent activated droplet sorting (FADS) systems to integrate the use of 4 independent fluorescence-exciting lasers (e.g., 405, 488, 561, 637 nm) in a single platform to make it compatible with different fluorescence-emitting biosensors. For this sorter, both hardware and software are provided and optimized for effortlessly sorting droplets at 60 Hz. Then, a passive droplet merger was also integrated into our method to enable adding new reagents to already made droplets at a rate of 200 Hz. Finally, we provide an optimized recipe for manufacturing these chips using silicon dry-etching tools. Because of the overall integration and the technical details presented here, our approach allows biologists to quickly use microfluidic technologies and achieve both single-cell resolution and high-throughput (> 50,000 cells/day) capabilities to mining and bioprospecting metagenomic data. © 2021, CC BY-NC-SA.

关键词： Cell proliferation

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Multi-view ontology alignment visualization (MOAV): A human-cognition based ontology alignment visualization technique

Journal of Network Intelligence

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Journal of Network Intelligence 2020年第4期5卷 198-210页

作者： Chen, Jie Zhang, Jie Xue, Xingsi Huang, Yikun Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.33 Xueyuan Road University Town Minhou FuzhouFujian350118 China School of Computer Science and Engineering Yulin Normal University No.299 Education Middle Road Yulin CityGuanxi Province537000 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Intelligent Information Processing Research Center Fujian University of Technology No.33 Xueyuan Road University Town Minhou FuzhouFujian350118 China Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology No.1 Jinji Road Guangxi Guilin541004 China Department of Information Technology Concord University College Fujian Normal University No.68 Xueyuan Road University Town Minhou FuzhouFujian350117 China

To further improve the quality of ontology alignment, it is necessary for an ontology matcher to introduce a user’s knowledge into its automatic matching process, which yields the development of interactive ontology matching techniques. Since validating problematic entity correspondences is a difficult cognition task, user interaction based on Ontology Alignment Visualization (OAV) has become the critical component of an interactive ontology matcher, which directly affects the quality of validating result and the efficiency of validating process. The existing OAV tools are mainly developed based on their designers’ subjective feelings and experiences, which do not take into consideration the law of human cognition. To improve the efficiency of the ontology alignment validating process, in this paper, a Multi-view OAV (MOAV) is proposed, which synthetically utilizes the human cognitive theory-information visualization and human-computer inter-action. The experiment utilizes the Ontology Alignment Evaluation Initiative (OAEI)’s benchmark to test the performance of our proposal, and the experimental results show that MOAV can effectively improve a user’s validating efficiency. © 2020, Taiwan Ubiquitous Information. All rights reserved.

关键词： Visualization

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Disease trajectory visualization system based on big data analytics 18

Disease trajectory visualization system based on big data an...

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2nd International Conference on Medical and Health Informatics, ICMHI 2018

作者： Chien, Ting-Ying Chenî, Chong-Yi Jinî, Guo-Lun Ting, Hsien-Wei Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan Department of Neurosurgery Taipei Hospital Ministry of Health and Welfare New Taipei City Taiwan

ISBN: (纸本)9781450363891

Background: For health promotion, recognition of risk factors that may modify health needs is necessary, and people then have the opportunity to change their behavior if they are aware of the risk factors. big data analysis of health data is emerging as a new trend in research that may lead to discovery of unknown facts, and visualization of data is a convenient way in which to understand the characteristics of the data. This study used the National Health Insurance Research database (NHIRD) as the basis to construct a visualization model for disease trajectory analysis. Methods: The NHIRD, which includes inpatient expenditure by admission (DD) and ambulatory care expenditure by visit (CD) data, was used in this study. We analyzed the medical care of patients, such as medications, surgery, medical expenditure and total length of stay, and calculated the number of patients who suffered other diseases within the next 6 months, 12 months and 24 months. A Sankey diagram was employed to show the disease trajectory. Based on data analytics, we constructed a system that helps the user to easily understand the disease trajectory. System implementation: The system includes two panels, a user input panel and an output panel. Using the input panel, the user can input basic information, such as gender, age, date and the queried disease. Based on user input and analytical models, the system will show the detailed trajectory for each queried disease, such as medical expenditure, medications and total length of stay in hospital, and uses Sankey diagrams to show the disease trajectory. Conclusions: This study constructed a visualization system based on analysis of the NHIRD. The Taiwan National Health Insurance Administration, Ministry of Health and Welfare, constructed the Health Bank, which contains health data of all individuals. This method may enable prediction of the risks of diseases in the future. © 2018 Association for Computing Machinery.

关键词： big data

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