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8th International Conference on Medical and Health informatics, ICMHI 2024

作者： Li, Ai-Hsien Adams Aruperes, Cristian Daniel Lai, Yen-Jun Chien, Ting-Ying Chiu, Yen-Ling Chan, Chien-Lung Division of Cardiology Far Eastern Memorial Hospital New Taipei TaiwanGraduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan TaiwanDepartment of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan TaiwanInnovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan TaiwanGraduate Institute of Medicine Yuan Ze University Taoyuan Taiwan TaiwanDepartment of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400716874

In the realm of research, the global health challenge posed by lung cancer remains pronounced, contributing substantially to annual cancer-related fatalities. The critical imperative lies in the early identification of pulmonary nodules, frequently indicative of impending lung cancer, to enhance patient outcomes and diminish mortality rates. Computed Tomography (CT) imaging stands out as a pivotal diagnostic instrument for the timely detection of these nodules. The swift proliferation of medical imaging data has underscored the pressing necessity for precise and efficient methodologies dedicated to nodule segmentation and measurement. These approaches are crucial in assisting radiologists in their diagnostic and clinical decision-making endeavors. In this study, we introduced a thorough method for analyzing lung nodules, leveraging dataset from Far Eastern Memorial Hospital (FEMH) comprising original CT images and manually annotated ground truth masks obtained with the assistance of radiologists at FEMH. This dataset is utilized for the segmentation of nodules. We employed advanced deep learning models, specifically the U-Net architecture, identified as the optimal model through our training process. We made substantial progress in nodule segmentation, attaining an Intersection over Union (IoU) score of 0.824 and a Dice Coefficient of 0.903 for the FEMH dataset. Furthermore, our performance improved when utilizing the merged dataset comprising FEMH and Luna16, yielding an IoU score of 0.862 and a Dice Coefficient of 0.926. Luna16 has been extensively utilized in numerous studies related to nodule detection and segmentation. In the next phase of the study, the best-performing model from our segmentation phase was utilized to predict nodule masks on the merged dataset. Subsequently, we measured the size of each predicted nodule by comparing it with the size ground truth mask in millimeters. In detail, this study achieved the Pearson Correlation Coefficient (PCC) at 0.

关键词： Computerized tomography

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Market Basket Analysis Using Apriori and FP-Growth for Analysis Consumer Expenditure Patterns at Berkah Mart in Pekanbaru Riau

Market Basket Analysis Using Apriori and FP-Growth for Analy...

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2018 Workshop on Multidisciplinary and Its Applications: Applied Mathematics, Computer science, Information Systems, and Information Technology, WMA-Mathcomtech 2018

作者： Mustakim Herianda, Della Maulina Ilham, Ahmad Daeng Gs, Achmad Laumal, Folkes E. Kurniasih, Nuning Iskandar, Akbar Manulangga, Gloria Indra Iswara, Ida Bagus Ary Rahim, Robbi Puzzle Research Data Technology UIN Sultan Syarif Kasim Riau Indonesia Department of Information System UIN Sultan Syarif Kasim Riau Indonesia Informatics Department Universitas Muhammadiyah Semarang Semarang Indonesia Department of Management University of 45 Surabaya Surabaya Indonesia Department of Electrical Engineering Politeknik Negeri Kupang Kupang Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Informatics Engineering STMIK AKBA Makassar Indonesia Informatics Department STMIK STIKOM Indonesia Denpasar Indonesia School of Computer and Communication Engineering Universiti Malaysia Perlis Kubang Gajah Malaysia

Berkah Mart is one of the new minimarkets in Pekanbaru city which began to develop as a retail business to meet the needs of the community. In his operation, Berkah Mart still uses the traditional store management perception that the placement of product layout on sale shelves has not been through sales analysis. Existing products have not been reviewed from the consumer's point of view, whereas the marketing economics concept provides a variety of product and consumer management methods for business management in Berkah Mart, including market bucket analysis techniques and various itemset search algorithms. In this research, Market Basket Basket Analysis with FP-Growth algorithm is proposed to determine the layout and planning of goods availability. The application of FP-Growth algorithm proved to be useful in generating many and informative association rules to find out the consumer spending pattern at Berkah Mart in Pekanbaru. Moreover, from the rules of the customer association can be segmented separately to meet the specific needs of customers with cost-effective by using some special promotions for the general group. The experimental results show that FP-Growth algorithm can analyze quickly and efficiently informing consumer shopping pattern at Berkah Mart and can increase Berkah Mart revenue. © Published under licence by IOP Publishing Ltd.

关键词： Sales

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ConvNeXt Model for Breast Cancer Image Classification

ConvNeXt Model for Breast Cancer Image Classification

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Devin Setiawan Andrea Stevens Karnyoto Indo Intan Bens Pardamean Department of Electrical Engineering and Computer Science University of Kansas Lawrence United States Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Informatics Engineering Universitas Dipa Makassar Makassar Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Breast cancer is an occurrence of cancer that attacks breast tissue and is the most common cancer among women worldwide, affecting one in eight women. In this modern world, breast cancer image classification simplifies the process of analyzing, providing objective and accurate results. By leveraging machine learning algorithms and computer vision techniques, we developed breast cancer detection. The dataset is histopathology dataset from BreakHis and UNHAS Hospital. We chose the ConvNeXt-Tiny model then modified the classifier head as the proposed method. Before the dataset is processed by the model, we augment the images by applying random horizontal and vertical flips, adjustments to brightness, contrast, saturation, and hue using color jitter. The augmentation process simulates real-world variance and enhances the model's ability to generalize to unseen data. Our proposed model gained better performance (accuracy, F1-Score) results compared two other techniques to VGG16 and SVM. According to our experiments, the F1-Score for the ConvNeXt-Tiny model with classifier head modification is higher at 0.9516, than the gain for VGG16 at 0.9292, and the gain for the SVM at 0.83.

关键词： Support vector machines Accuracy Head Computational modeling Transfer learning Breast cancer Magnetic heads data models Reliability Image classification

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Development of Automated Detection Methods for Calculating Blood Vessel Width and Stenosis Rate in Cardiac Catheterization Imaging 24

Development of Automated Detection Methods for Calculating B...

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8th International Conference on Medical and Health informatics, ICMHI 2024

作者： Huang, Hsien-Cheng Chang, Hsiao-Huang Ke, Xiang-Wei Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Cardiovascular Surgery Department Taipei Veterans General Hospital Taipei City Taiwan Taipei Medical University Taipei City Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

Coronary artery disease is the most prevalent type of heart disease and has a considerable mortality rate. In diagnosing and assessing coronary artery disease, physicians must integrate a variety of clinical information and test results to evaluate the degree of coronary artery obstruction and continually learn and improve their skills in clinical practice to ensure the most accurate assessment of patients. Currently, the use of information technology to assist medical staff allows more effective and accurate analysis of symptoms. This approach not only serves as an auxiliary standard for physician diagnosis, thereby reducing the possibility of misdiagnosis, but also significantly improves patient treatment outcomes and medical quality while reducing the time burden on physicians for diagnosing patients. This study utilized image processing techniques and thinning algorithms to automate the detection of coronary artery width and, following the recommended methods of physicians in related fields, used specific vessel widths to calculate the degree of coronary artery narrowing. This provides a more accurate and efficient assessment method for evaluating the degree of vascular constriction in patients with coronary artery disease. © 2024 Copyright ACM.

关键词： Patient treatment

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A Clinical Decision Support System of Pressure Ulcers Tissue Classification 21

A Clinical Decision Support System of Pressure Ulcers Tissue...

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5th International Conference on Medical and Health informatics, ICMHI 2021

作者： Chang, Dun-Hao Chu, Po-Jui Li, Yi-Jhen Ning, Chun-Kai Chien, Ting-Ying Department of Information Management Yuan Ze University Taoyuan City Taiwan Department of Plastic and Aesthetic Surgery Far Eastern Memorial Hospital Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9781450389846

Pressure ulcers (PUs) are a common problem associated with great morbidity and cost. Because of the lack of professional personnel in the institutions or home care system, the diagnosis and treatment of PUs were sometimes delayed. We aim to develop an automatic tissue classification and severity evaluation system of PUs using AI technology. All PU images were collected from patients in the Far Eastern Memorial Hospital (FEMH) in recent 3 years and labeled according to different tissue types. The labeled images were used to train the tissue segmentation model with U-net convolutional neural network (CNN). The percentage of the different tissue can be calculated automatically and would be compared with the manually labeled one. The output of tissue classification was transformed to a clinical decision support system (CDSS) with a rule-base formula. Our study showed that proposed system can classify each tissue automatically and reach an accuracy about 93.6%. However, the CDSS's accuracy rate was only 64% for the referral suggestion and 62% for the care recommendation. Hence, the system should be modified with more data and information. © 2021 ACM.

关键词： Tissue

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Ernst Denert Award for Software Engineering 2019 1

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1000年

作者： Michael Felderer Wilhelm Hasselbring Heiko Koziolek Florian Matthes Lutz Prechelt Ralf Reussner Bernhard Rumpe Ina Schaefer

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Ernst Denert Award for Software Engineering 2020 1

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1000年

作者： Michael Felderer Wilhelm Hasselbring Heiko Koziolek Florian Matthes Lutz Prechelt Ralf Reussner Bernhard Rumpe Ina Schaefer

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E-Government Interoperability: Provincial-Level Architecture Model to Enable Fast Healthcare Interoperability Resources (FHIR)

E-Government Interoperability: Provincial-Level Architecture...

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International Conference on Information Management and Technology (ICIMTech)

作者： Diana Wijayanti Satriawansyah Urbaya Taufiq Sitompul Verry Adrian Department of Information Systems BINUS Graduate Program Bina Nusantara University Jakarta Indonesia Country Health Information Systems and Data Use (CHISU) Program USAID Indonesia Jakarta Indonesia Department of Informatics University of Oslo Oslo Norway Computer Science Department Binus Graduate Program – Doctor Computer Science Bina Nusantara University Jakarta Indonesia Center of Data and Information Technology Jakarta Provincial Health Office Jakarta Indonesia

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

The process of using ICT to provide services to the public is known as the Indonesian e-Government system, or Sistem Pemerintahan Berbasis Elektronik (SPBE). The e-Government initiative in Jakarta Provincial Health Office involves enhancing collaboration among public health entities for efficient data exchange and streamlined processes, especially between the Provincial and District Health Offices, public hospitals, government clinics, and primary health care centers (Puskesmas). Achieving interoperability requires standardized protocols and a well-defined architectural model to integrate data seamlessly. This study presents a provincial-level architectural model focused on improving electronic health records interoperability, aiming to promote the adoption of the national Fast Healthcare Interoperability Resources (FHIR) health information exchange platform and enhance the integrity of health data in Jakarta. The study methodology involves conducting literature reviews, observations, and discussions with representatives from healthcare facilities to develop the e-Government architecture model and prototype of the infrastructure layer aiming to facilitate the interoperability of Electronic Health Records (EHRs) across 93 healthcare facilities, all of which are part of the SPBE users.

关键词： Prototypes Medical services data models Security Public healthcare Electronic medical records Interoperability Information exchange Standards Electronic government

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Automated, high throughput exploration of process–structure–property relationships using the MapReduce paradigm

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Materials Discovery 2015年 1卷 21-28页

作者： Wodo, Olga Zola, Jaroslaw Sarath Pokuri, Balaji Sesha Du, Pengfei Ganapathysubramanian, Baskar University at Buffalo Materials Design and Innovation Department Buffalo United States University at Buffalo Mechanical and Aerospace Engineering Department Buffalo United States University at Buffalo Computational and Data-Enabled Science and Engineering Program Buffalo United States University at Buffalo Computer Science and Engineering Department Buffalo United States University at Buffalo Biomedical Informatics Department Buffalo United States Iowa State University Mechanical Engineering Department Ames United States Iowa State University Electrical and Computer Engineering Department Ames United States

The microstructure of a material intimately affects the performance of a device made from this material. The microstructure, in turn, is affected by the processing pathway used to fabricate the device. This forms the process–structure–property triangle that is central to material science. There has been increasing interest to comprehensively understand and subsequently exploit process–structure–property (PSP) relationships to design processing pathways that result in tailored microstructures exhibiting optimal properties. However, unraveling process–structure–property relationships usually requires systematic and tedious combinatorial search of process and system variables to identify the microstructures that are produced. This is further complicated by the necessity to interrogate the properties of the huge set of corresponding microstructures. Motivated by this challenge, we focus on developing a generic methodology to establish and explore PSP pathways. We leverage recent advances in high performance computing (HPC) and high throughput computing (HTC) with the premise that a domain expert should be able to focus on domain specific PSP problems while the highly specialized HPC/HTC knowledge needed to approach such problems should be hidden from the domain expert. Our hypothesis is that PSP exploration can be naturally formulated in terms of a standard paradigm in cloud computing, namely the MapReduce programming model. We show how reformulating PSP exploration into a MapReduce workflow enables us to take advantage of advances in cloud computing while requiring minimal specialized knowledge of HPC. We illustrate this generic approach by exploring PSP relationships relevant to organic photovoltaics. We focus on identifying microstructural traits that correlate with specific properties of the photovoltaic process: exciton generation, exciton dissociation and charge generation. We integrate a graph-based microstructure characterization tool, and a microstructure-aware

关键词： Drift diffusion model High throughput analysis MapReduce paradigm Organic solar cells Process–structure–processing Thermal annealing

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ENIGMA-Meditation: Worldwide Consortium for Neuroscientific Investigations of Meditation Practices

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Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2025年第4期10卷 425-436页

作者： Ganesan, Saampras Barrios, Fernando A. Batta, Ishaan Bauer, Clemens C.C. Braver, Todd S. Brewer, Judson A. Brown, Kirk Warren Cahn, Rael Cain, Joshua A. Calhoun, Vince D. Cao, Lei Chetelat, Gaël Ching, Christopher R.K. Creswell, J. David Dagnino, Paulina Clara Davanger, Svend Davidson, Richard J. Deco, Gustavo Dutcher, Janine M. Escrichs, Anira Eyler, Lisa T. Fani, Negar Farb, Norman A.S. Fialoke, Suruchi Fresco, David M. Garg, Rahul Garland, Eric L. Goldin, Philippe Hafeman, Danella M. Jahanshad, Neda Kang, Yoona Khalsa, Sahib S. Kirlic, Namik Lazar, Sara W. Lutz, Antoine McDermott, Timothy J. Pagnoni, Giuseppe Piguet, Camille Prakash, Ruchika S. Rahrig, Hadley Reggente, Nicco Saccaro, Luigi F. Sacchet, Matthew D. Siegle, Greg J. Tang, Yi-Yuan Thomopoulos, Sophia I. Thompson, Paul M. Torske, Alyssa Treves, Isaac N. Tripathi, Vaibhav Tsuchiyagaito, Aki Turner, Matthew D. Vago, David R. Valk, Sofie Zeidan, Fadel Zalesky, Andrew Turner, Jessica A. King, Anthony P. Department of Biomedical Engineering The University of Melbourne Carlton VIC Australia Contemplative Studies Centre Melbourne School of Psychological Sciences The University of Melbourne Melbourne VIC Australia Systems Lab of Neuroscience Neuropsychiatry and Neuroengineering The University of Melbourne Parkville VIC Australia Universidad Nacional Autónoma de México Instituto de Neurobiolgía México Querétaro Mexico Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology and Emory University Atlanta GA United States Department of Psychology Northeastern University Boston Massachusetts Brain and Cognitive Science McGovern Institute for Brain Research Massachusetts Institute of Technology Cambridge Massachusetts Department of Psychological and Brain Sciences Washington University St. Louis Missouri Department of Behavioral and Social Sciences Brown University School of Public Health Providence Rhode Island Department of Psychology Carnegie Mellon University Pittsburgh PA United States University of Southern California Department of Psychiatry & Behavioral Sciences Los Angeles California University of Southern California Center for Mindfulness Science Los Angeles California Institute for Advanced Consciousness Studies Santa Monica California Department of Psychiatry and Behavioral Health The Ohio State University College of Medicine Columbus OH United States Normandie University Université de Caen Normandie INSERM U1237 Neuropresage Team Cyceron Caen France Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of the University of Southern California Los Angeles California Desert-Pacific Mental Illness Research Education and Clinical Center VA San Diego Healthcare System San Diego California Computational Neuroscience Group Center for Brain and Cognition Department of Information and Communication Technologies Universitat Pompeu Fa

Meditation is a family of ancient and contemporary contemplative mind-body practices that can modulate psychological processes, awareness, and mental states. Over the last 40 years, clinical science has manualized meditation practices and designed various meditation interventions that have shown therapeutic efficacy for disorders including depression, pain, addiction, and anxiety. Over the past decade, neuroimaging has been used to examine the neuroscientific basis of meditation practices, effects, states, and outcomes for clinical and nonclinical populations. However, the generalizability and replicability of current neuroscientific models of meditation have not yet been established, because they are largely based on small datasets entrenched with heterogeneity along several domains of meditation (e.g., practice types, meditation experience, clinical disorder targeted), experimental design, and neuroimaging methods (e.g., preprocessing, analysis, task-based, resting-state, structural magnetic resonance imaging). These limitations have precluded a nuanced and rigorous neuroscientific phenotyping of meditation practices and their potential benefits. Here, we present ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)–Meditation, the first worldwide collaborative consortium for neuroscientific investigations of meditation practices. ENIGMA-Meditation will enable systematic meta- and mega-analyses of globally distributed neuroimaging datasets of meditation using shared, standardized neuroimaging methods and tools to improve statistical power and generalizability. Through this powerful collaborative framework, existing neuroscientific accounts of meditation practices can be extended to generate novel and rigorous neuroscientific insights that account for multidomain heterogeneity. ENIGMA-Meditation will inform neuroscientific mechanisms that underlie therapeutic action of meditation practices on psychological and cognitive attributes, thereby advancing the f

关键词： Consortium ENIGMA Magnetic resonance imaging (MRI) Meditation Mindfulness Neuroimaging

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