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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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Exploring Approaches to Answer Biomedical Questions: From Pre-processing to GPT-4 Notebook for the BioASQ Lab at CLEF 2023 24

Exploring Approaches to Answer Biomedical Questions: From Pr...

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24th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF-WN 2023

作者： Kim, Hyunjae Hwang, Hyeon Lee, Chaeeun Seo, Minju Yoon, Wonjin Kang, Jaewoo Department of Computer Science and Engineering Korea University Seoul02841 Korea Republic of Computational Health Informatics Program Boston Children’s Hospital BostonMA02115 United States Harvard Medical School BostonMA02115 United States AIGEN Sciences Seoul04778 Korea Republic of

Biomedical question answering (QA) plays a crucial role in assisting researchers, healthcare professionals, and even patients in accessing and retrieving accurate and up-to-date information from the vast amount of biomedical knowledge available in literature. To enhance the efficiency of knowledge discovery and information retrieval, we investigate the efficacy of various pre-processing, model training, data augmentation, and ensemble methods and evaluate a range of advanced pre-trained models such as BioLinkBERT and GPT-4. Additionally, we explore data augmentation and ensemble methods to further improve system performance. In our participation in BioASQ Task 11b-Phase B, our systems achieved a top ranking in all four batches for the yes/no type of questions, in one out of four batches for factoid questions, and in two out of four batches for list-type questions. © 2023 Copyright for this paper by its authors.

关键词： BioASQ 11b BioLinkBERT Data Augmentation Ensemble GPT-4

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Slice-Fusion: Reducing False Positives in Liver Tumor Detection for Mask R-CNN

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2023年第5期20卷 3267-3277页

作者： Tu, Deng-Yao Lin, Peng-Chan Chou, Hsin-Hung Shen, Meng-Ru Hsieh, Sun-Yuan National Cheng Kung University Master Degree Program on Artificial Intelligence Tainan City70101 Taiwan National Cheng Kung University Institute of Medical Informatics Department of Oncology Department of Genomic Medicine National Cheng Kung University Hospital College of Medicine Department of Computer Science and Information Engineering Tainan City70101 Taiwan National Chi Nan University Department of Computer Science and Information Engineering Nantou County54561 Taiwan National Cheng Kung University Graduate Institute of Clinical Medicine Department of Obstetrics and Gynecology Department of Pharmacology National Cheng Kung University Hospital College of Medicine Tainan City70101 Taiwan National Cheng Kung University Institute of Medical Information Institute of Manufacturing Information and Systems Center for Innovative FinTech Business Models International Center for the Scientific Development of Shrimp Aquaculture Department of Computer Science and Information Engineering Tainan City70101 Taiwan

Automatic liver tumor detection from computed tomography (CT) makes clinical examinations more accurate. However, deep learning-based detection algorithms are characterized by high sensitivity and low precision, which hinders diagnosis given that false-positive tumors must first be identified and excluded. These false positives arise because detection models incorrectly identify partial volume artifacts as lesions, which in turn stems from the inability to learn the perihepatic structure from a global perspective. To overcome this limitation, we propose a novel slice-fusion method in which mining the global structural relationship between the tissues in the target CT slices and fusing the features of adjacent slices according to the importance of the tissues. Furthermore, we design a new network based on our slice-fusion method and Mask R-CNN detection model, called Pinpoint-Net. We evaluated proposed model on the Liver Tumor Segmentation Challenge (LiTS) dataset and our liver metastases dataset. Experiments demonstrated that our slice-fusion method not only enhance tumor detection ability via reducing the number of false-positive tumors smaller than 10mm, but also improve segmentation performance. Without bells and whistles, a single Pinpoint-Net showed outstanding performance in liver tumor detection and segmentation on LiTS test dataset compared with other state-of-the-art models. © 2004-2012 IEEE.

关键词： Tumors

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AlexNet Architecture Based Convolution Neural Network for Realtime Audio to Text Translator of Bisindo Hand Sign

AlexNet Architecture Based Convolution Neural Network for Re...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Dhiki Sujatmiko Christy Atika Sari Eko Hari Rachmawanto Andi Danang Krismawan Bilal R. Altamer Mohamed Ayad Alkhafaji Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Study Program in Animation University of Dian Nuswantoro Semarang Indonesia Department of Computer Science Mosul University Presidency University of Mosul Mosul Iraq Department of Computer Science National University of Science and Technology DhiQar Iraq

Deafness is a condition that results in the loss of hearing function, hindering the reception of information such as oral communication that relies on auditory senses. Consequently, individuals with hearing impairment experience communication barriers and may have limited or no ability to respond. One solution is the use of sign language. In Indonesia, there are two known sign languages: Sibi and Bisindo. Both serve the same function but differ in their style of movement and expression. Bisindo is considered more flexible as it conveys meaning based on the Indonesian language. However, the universal understanding of this language solution is still limited among many people. Therefore, a program is needed to facilitate translation between deaf individuals who use sign language and their counterparts who do not communicate through sign language. CNN (Convolutional Neural Network) is a deep learning algorithm used for training visual input data recognition by computer systems. There are various CNN-based architectures, and one of them is AlexNet. Based on the author's testing, the AlexNet architecture proves to be suitable for real-time sign language translation. The evaluation of the system involved 7,800 datasets and 520 testing instances, with an average accuracy of 468 correct translations. When averaged, the system achieved a 90% accuracy rate, representing a 100% increase in accuracy compared to previous approaches.

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Development and Validation of Customer Intention Factors for Live-Stream Shopping 7

Development and Validation of Customer Intention Factors for...

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7th International Conference on New Media Studies, CONMEDIA 2023

作者： Fernando, Erick Hidayat, Desman Pengaribuan, Christian Haposan Prabowo, Yulius Denny Universitas Multimedia Nusantara Faculty of Engineering and Informatics Department of Information System Tangerang Indonesia Bina Nusantara University Binus Entrepreneurship Center Management Department Jakarta11480 Indonesia Sampoerna University Management Study Program Faculty of Business Jakarta12780 Indonesia Bina Nusantara University Binus Online Learning Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307504

Along with technological improvements, online shopping is currently developing quickly. Online shops started by selling electronics, clothing, food, and home appliances and continue to evolve, selling various things. Currently, digital marketing has a novel technique, namely live shopping, where sellers can present, promote and offer their products directly through live streaming on social media and e-commerce platforms. This research will focus on gaining insight into the factors influencing customers to watch and purchase through live streaming. This research is an early-stage study of exploring and searching for research models. The study used a literature review approach by reviewing similar previous research articles. Several variable components were used in this study. The findings of the factors include visibility capacity, meta-vocation capacity, purchase orientation capacity, social presence, price, perceived product quality, perceived enjoyment, purchase orientation, perceived usefulness, and intention to buy. These factors were tested and proved to be valid with Cronbach's Alpha value being more than 0.5 and reliable with the value of the loading factor a greater than 0.6. Criteria The factors of the findings for further analysis of the relationship between factors and other variables. © 2023 IEEE.

关键词： Domestic appliances

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The Implementation of TOGAF for Enterprise System Planning on XYZ Campus

The Implementation of TOGAF for Enterprise System Planning o...

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Networking, Electrical Engineering, computer science, and Technology (IConNECT), International Conference on

作者： Tamsir Hasudungan Sirait Ford Lumban Gaol Suhono Harso Supangkat Benny Ranti Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Bina Nusantara University Jakarta Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Computer Science Department Universitas Indonesia Jakarta Indonesia

Universities can employ information technology as one means of achieving their goals and objectives. Universities can get advantages from information technology, such as effective resource management and information management for decision-making. However, don't forget that the campus must consider what technology is appropriate to assist them achieve their goals, particularly in the current industrial era 4.0 where technology is available with many different choices. The campus requires an enterprise architecture in order to design, manage, and coordinate information technology infrastructure, applications, and processes strategically and thoroughly. The adoption of enterprise information system architecture (EA) is also intended to improve the quality of services provided to internal and external stakeholders. In this case, Enterprise Architecture can help an organization to match its information technology resources with business processes and strategies to achieve their goals. This research was conducted using TOGAF ADM, also known as the Open Group Architecture Framework Architecture Development Method. This method offers best practices for creating enterprise architecture and emphasizes several steps that include creating an architectural vision, information systems, business architecture modeling to help XYZ campus manage all their information technology.

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Dynamic Healthcare Embeddings for Improving Patient Care 14

Dynamic Healthcare Embeddings for Improving Patient Care

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14th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022

作者： Jang, Hankyu Lee, Sulyun Hasibul Hasan, D.M. Polgreen, Philip M. Pemmaraju, Sriram V. Adhikari, Bijaya University of Iowa Department of Computer Science Iowa CityIA United States University of Iowa Interdisciplinary Graduate Program in Informatics Iowa CityIA United States University of Iowa Department of Internal Medicine Iowa CityIA United States

ISBN: (纸本)9781665456616

As hospitals move towards automating and integrating their computing systems, more fine-grained hospital operations data are becoming available. These data include hospital architectural drawings, logs of interactions between patients and healthcare professionals, prescription data, procedures data, and data on patient admission, discharge, and transfers. This has opened up many fascinating avenues for healthcare-related prediction tasks for improving patient care. However, in order to leverage off-the-shelf machine learning software for these tasks, one needs to learn structured representations of entities involved from heterogeneous, dynamic data streams. Here, we propose DECENT, an auto-encoding heterogeneous co-evolving dynamic neural network, for learning heterogeneous dynamic embeddings of patients, doctors, rooms, and medications from diverse data streams. These embeddings capture similarities among doctors, rooms, patients, and medications based on static attributes and dynamic interactions. DECENT enables several applications in healthcare prediction, such as predicting mortality risk and case severity of patients, adverse events (e.g., transfer back into an intensive care unit), and future healthcare-associated infections. The results of using the learned patient embeddings in predictive modeling show that DECENT has a gain of up to 48.1% on the mortality risk prediction task, 12.6% on the case severity prediction task, 6.4% on the medical intensive care unit transfer task, and 3.8% on the Clostridioides difficile (***) Infection (CDI) prediction task over the state-of-the-art baselines. In addition, case studies on the learned doctor, medication, and room embeddings show that our approach learns meaningful and interpretable embeddings. © 2022 IEEE.

关键词： Heterogeneous networks

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Classification of Highly Similar Fish Species Using Machine Learning

Classification of Highly Similar Fish Species Using Machine ...

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International Conference on Systems, Signals and Image Processing, IWSSIP

作者： Leandro Magno C. Silva Franklin Cesar Flores Rosilene Luciana Delariva Gustavo Z. Felipe Yandre M. G. Da Costa Graduate Program in Computer Science State University of Maringá Federal Institute of Paraná Maringá Brazil Department of Informatics State University of Maringá Maringá Brazil Center for Biological and Health Sciences State University of Western Paraná Cascavel Brazil Graduate Program in Computer Science State University of Maringá Maringá Brazil

ISBN: (数字)9798350391886

ISBN: (纸本)9798350391893

Fish species inventory and monitoring is a timely concern due to the increasing environmental imbalance. In this sense, the development of machine learning strategies aiming to support automatic fish species identification is an important contribution, both in environmental and scientific terms. This study explores handcrafted features aiming to perform fish species identification in a quite challenging scenario, in which the inter-classes differences are minimal, as the species belong to the same family considering the hierarchy of biological classification (i.e. Characidae). For this purpose, it is proposed a set of features designed properly, aiming to capture visual properties closely related to each species that are difficult to be noted by experts in this domain of investigation. An additional contribution of this work is a dataset of 4870 images taken from 416 individuals from nine fish species. The Region of Interest (RoI) corresponding to the fish body is segmented, and horizontal and vertical histograms are collected from each sample, aiming to capture information related to the body shape, and other characteristics particular to each species. Experiments are performed using the Support Vector Machine (SVM), K-Nearest Neighbours (K-NN), Random Forest and Naive-Bayes classifiers and, in the best scenario, it is obtained an accuracy rate of 99%. This result corroborates the hypothesis that the use of handcrafted features specifically developed for this type of application can be quite effective in addressing this classification problem.

关键词： Support vector machines Image segmentation Visualization Histograms Image recognition Shape Fish

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Twitter Data Sentiment Analysis of COVID-19 Vaccination using Machine Learning 5

Twitter Data Sentiment Analysis of COVID-19 Vaccination usin...

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5th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2022

作者： Maulana, Fairuz Iqbal Adi, Puput Dani Prasetyo Lestari, Dian Purnomo, Agung Prihatin, Sukeipah Yuli School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Telecommunication Research Center National Research and Innovation Agency Jakarta11480 Indonesia Airlangga University Faculty of Medicine Surabaya60132 Indonesia BINUS Business School Undergraduate Program Bina Nusantara University Entrepreneurship Department Jakarta11480 Indonesia Informatics Study Program Politeknik Kota Malang Malang Indonesia

ISBN: (纸本)9781665455121

The expansion of the web is accelerating, which helps encourage the creation of fresh ideas. In today's internet era, we must suggest techniques to filter out various information. Social media sentiment analysis based on Twitter data can monitor the real-Time monitoring of the COVID-19 vaccine. In this way, relevant organizations or governments can take proactive steps to address misinformation and inappropriate behaviour around the COVID-19 vaccine, which threatens the success of the national vaccination campaign. The purpose of this research is to determine if there is a link between how people feel about the COVID-19 vaccine on Twitter and how many people actually get vaccinated against it. This study uses the COVID-19 All Vaccines Tweet dataset sourced from Kaggle. This research Identifies public sentiment, emotion, word usage, and trend of all filtered tweets. The results show that there are 31% positive tweets, 10% negative tweets, and 58% neutral tweets. Tweets with neutral subjective valence tend to cluster in the middle of the polarity scale (between-1 and +1), whereas tweets with strong subjective valence are spread across the scale. © 2022 IEEE.

关键词： COVID-19

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Feature Selection and Classification Algorithm Approaches for Parkinson's Disease Detection 5

Feature Selection and Classification Algorithm Approaches fo...

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5th IEEE International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2021

作者： Marcos, Hendra Warsito, Budi Wibowo, Adi Diponegoro University Doctoral Program Information System Department Informatics Universitas Amikom Purwokerto Purwokerto Indonesia Diponegoro University Faculty Of Science And Mathematics Department Of Statistics Semarang Indonesia Department Of Computer Science Informatics Diponegoro University Semarang Indonesia

ISBN: (纸本)9781665401968

Millions of people worldwide have developed Parkinson's disease (PD), which is more common by 50. Advances in information technology, especially in the field of machine learning, are being challenged for the development of early detection algorithms. This helps doctors to detect disease early and more accurately based on data. The available data has large dimensions and features so that the feature selection method can be used. This research paper aims to provide insight by comparing several algorithms for feature selection and PD classification. PD classification can help improve treatment efficiency and save time. The problem that arises is how the classification algorithm can show better and more efficient accuracy results. A large number of datasets is one of the challenges in the development of classification algorithms. The voice input dataset was obtained from the UCI machine learning repository. Bagging's algorithm out performed on average 100%, and MLP 96.58%, and RF 92.52%. © 2021 IEEE.

关键词： Classification (of information)

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