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6th International Conference on Control and Robotics, ICCR 2024

作者： Hsun-Jui, Hu Yu-Hsien, Chiu Pei-Ching, Yang Chang Jung Christian University Master Degree Program in Information and Design Tainan Taiwan Kaohsiung Medical University Department of Healthcare Administration and Medical Informatics Kaohsiung Taiwan Artificial Intelligence Research Center Chang Jung Christian University Tainan Taiwan Chang Jung Christian University Department of Computer Science and Information Engineering Tainan Taiwan

ISBN: (纸本)9798331518158

Air pollution is a significant issue because it burdens human health and the environment. Particulate matter with a diameter of 2.5 micrometers or smaller (PM2.5) can penetrate the lungs and enter the bloodstream, seriously affecting the cardiovascular system, causing strokes, and impacting the respiratory system. Modern individuals spend 70% to 90% of their time indoors, making indoor air quality even more important than outdoor air quality. To estimate and predict indoor air quality, this study utilizes low-cost, self-assembled air boxes based on the Internet of Things to collect various air quality data. The collected data is then used in an indoor air quality forecasting system that combines Long Short-Term Memory (LSTM) and K-means clustering methods to compare the overall performance of the algorithms. The experimental results are analyzed and statistically described based on each model method prediction accuracy and clustering results. These methods are integrated to develop an indoor air quality forecasting system to help indoor populations prevent issues in advance or optimize energy usage. © 2024 IEEE.

关键词： Indoor air pollution

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Analysis of the Cyber Attacks in Indonesia's Health Sector in Industry 4.0 Era 8

Analysis of the Cyber Attacks in Indonesia's Health Sector i...

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8th IEEE International Conference on Computing, Engineering and Design, ICCED 2022

作者： Hidayat, Arif Mantoro, Teddy Susandi, Armi Indonesian State Intelligence College Medical Intelligence Post-Graduate Program Bogor Indonesia Sampoema University Media-Tech Lab Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9781665453899

At this time the world has undergone considerable changes, where the world has entered the era of the industrial revolution 4.0. With these changes, there are many positive impacts or opportunities that can support human activities in the economic, political, socio-cultural, health, and other fields. And alsothe author tries to analyze about industry 4.0 era for Indonesia's health sector with SWOT Analysis to know about Strenght, Weaknesses, Opportunities, and Threats. However, the challenges of technological advances can pose a threat to countries that are not ready to facethem, one of which is cyber attacks that can threaten data security. Therefore, it is necessary to be prepared in terms of data security by cooperating with various parties involved in it. © 2022 IEEE.

关键词： Industry 4.0

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Combining Weakly Supervised Segmentation with Multitask Learning for Improved 3D MRI Brain Tumour Classification 2nd

Combining Weakly Supervised Segmentation with Multitask Lea...

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2nd Workshop on medical Image Learning with Noisy and Limited Data, MILLanD 2023

作者： Rajapaksa, Sajith Namdar, Khashayar Khalvati, Farzad Neurosciences and Mental Health Research Program The Hospital for Sick Children Toronto Canada Institute of Medical Science University of Toronto Toronto Canada Department of Medical Imaging University of Toronto Toronto Canada Department of Computer Science University of Toronto Toronto Canada Department of Mechanical and Industrial Engineering University of Toronto Toronto Canada Vector Institute Toronto Canada

ISBN: (纸本)9783031471964

Segmentation plays a crucial role in computer-aided medical image diagnosis, as it enables the models to focus on the region of interest (ROI) and improve classification performance. However, medical image datasets often lack segmentation masks due to cost. In this work, we have introduced a novel end-to-end pipeline that enhances classification by incorporating ROIs generated by a weakly supervised method as an auxiliary task in a deep multitask learning framework. We have demonstrated that our approach outperforms conventional methods that only rely on classification. By using weakly supervised segmentation, we are able to leverage pixel-wise information, which ultimately leads to improved classification performance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image enhancement

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PacECG-Net: A Multi-modal Approach Integrating LLMs and ECG for LVSD Classification in Pacemaker Patients 25

PacECG-Net: A Multi-modal Approach Integrating LLMs and ECG ...

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40th Annual ACM Symposium on Applied Computing, SAC 2025

作者： Shim, Wonkyeong Park, Namjun Ko, Donggeun Kim, San Gwag, Hye Bin Park, Young Jun Park, Seung-Jung Kim, Jaekwang Department of Computer Science and Engineering Sungkyunkwan University Suwon Korea Republic of Aim Future AI Seoul Korea Republic of Dep. of Computer Science and Eng. Sungkyunkwan University Suwon Korea Republic of Samsung Changwon Hospital Changwon Korea Republic of Wonju Severance Christian Hospital Wonju Korea Republic of Samsung Medical Center Seoul Korea Republic of School of Convergence/Convergence Program for Social Innovation Sungkyunkwan University Seoul Korea Republic of

ISBN: (纸本)9798400706295

This study presents an AI-based model using ECG signals to predict left ventricular systolic dysfunction (LVSD) in pacemaker patients. A 1D convolutional neural network (CNN) combined with large language models processed both sequential ECG data and nonsequential clinical metadata. The model achieved an AUROC of 0.97 on both general and pacemaker-specific datasets, demonstrating its high accuracy. This approach offers a fast, cost-effective alternative to traditional echocardiography, improving LVSD detection in patients with pacemakers. Copyright © 2025 held by the owner/author(s).

关键词： Convolutional neural networks

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Misuse of electronic medical records in blockchain technology intelligence security system 8

Misuse of electronic medical records in blockchain technolog...

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8th IEEE International Conference on Computing, Engineering and Design, ICCED 2022

作者： Wattimena, Mirel Retnowati, Dwi Mantoro, Teddy State Intelligence College Medical Intelligence Postgraduate Program Bogor Indonesia Sampoerna University and State Intelligence College Media-Tech Lab Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9781665453899

Technological advances Globalization Era are very rapidly developing. The advancement of information technology in the health sector is Electronic medical Records. Electronic medical Record is a technology-based health information system that functions to store complete health and patient care data. most of the Electronic medical Records owned are simple and do not have a data security system. Data Security, Authenticity of management, and admin of data registration from health facilities can be a threat to the misuse of Electronic medical records. The purpose of maintaining the confidentiality of data using Blockchain Technology is used for the management of overall patient data protection. Simple Blockchain technology is recommended as an Electronic medical Record Security system and Blockchain technology as a Cryptographic-based data security system where each Blockchain block can impact patient data in detail supported by the Internet network. The method used is in the form of empowering simple Blockchain technology in supporting the Security system protection of personal data in Electronic medical Records which can increase the effectiveness of data storage so that it can avoid data misuse and manipulation of patient care data. Blockchain is a technology that is not easily accessed by others and helps protect patient data from forms of threats. In an effort to overcome the threat of misuse of Electronic medical Record data, the role of intelligence in the form of securing data security systems is a solution in protecting data through data security © 2022 IEEE.

关键词： Security systems

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Regulatory Support to Prevent Health Data Breaches 8

Regulatory Support to Prevent Health Data Breaches

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8th IEEE International Conference on Computing, Engineering and Design, ICCED 2022

作者： Firmansyah, Warmanto Mantoro, Teddy Persadha, Pratama Dahlian State Intelligence College Medical Intelligence Postgraduate Program Bogor Indonesia Sampoerna University and State Intelligence College Media-Tech Lab Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9781665453899

Digitalization of health data is one of the advances in the era of information technology. It is very useful for both the patient and the paramedic. Unfortunately, it also has a hole because there are more breaches due to the leakage of health data nowadays. Electronic health data is particularly vulnerable, as it is most often targeted by attackers or hackers. Health data were considered to have the most sensitive personal data. In Indonesia, there have been several leaks of health data. The breached data are from government-owned applications including BPJS Kesehatan, eHAC, and vaccine certificates. The health data needed to be protected, so there is no misuse of it. The breached health data can be harmful to the person affected. This study is to capture the situation and problem of health data breaches on the government-owned application in Indonesia. The result of this paper is to propose a recommendation to minimize the occurrence of health data breaches. Strategic moves are needed in anticipating the occurrence of health data breaches. One of the steps that can be taken is the preparation of regulations to secure health data. With the relevant regulations, it is projected that the breach of health data can be minimized. © 2022 IEEE.

关键词： Personal computing

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Advanced Receiver Operating Characteristic Curve Analysis to Identify Outliers in Binary Machine Learning Classifications for Precision Medicine

Advanced Receiver Operating Characteristic Curve Analysis to...

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2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Namdar, Khashayar Khalvati, Farzad Department of Diagnostic & Interventional Radiology The Hospital for Sick Children TorontoON Canada SickKids Research Institute Neurosciences & Mental Health Research Program TorontoON Canada Department of Medical Imaging University of Toronto TorontoON Canada Institute of Medical Science University of Toronto TorontoON Canada University of Toronto Department of Computer Science TorontoON Canada Department of Mechanical and Industrial Engineering University of Toronto TorontoON Canada Vector Institute TorontoON Canada

ISBN: (纸本)9798350351552

The Receiver Operating Characteristic (ROC) curve is a critical tool for binary classification analysis in medicine, with the Area Under the ROC Curve (AUROC) serving as a widely accepted metric to evaluate the performance of binary classifiers. This study conducts a comprehensive review of the ROC curve with a focus on its utility in outlier identification. We introduce a novel scoring method to rank actual positives and actual negatives within a test set, according to their impact on AUROC degradation. We bridge the scoring system with the ROC curve analysis to quantify each data point's contribution to AUROC loss. Furthermore, we introduce the IMICS ROC Analyzer, a graphical user interface-based software, embedded with our innovative algorithms. Through the use of an open-source prostate cancer dataset, we illustrate the application of our algorithms for practical outlier detection in binary classification tasks. The IMICS ROC Analyzer enhances the field of precision medicine by allowing for measuring an individual's contributions (be it patients, lesions, or samples) to the overall AUROC, thus facilitating confidence measurement of Machine Learning (ML) classifiers for individual cases of interest in a cohort. © 2024 IEEE.

关键词： Adversarial machine learning

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Prevention of Misuse of Information Technology in the Health Industry 8

Prevention of Misuse of Information Technology in the Health...

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8th IEEE International Conference on Computing, Engineering and Design, ICCED 2022

作者： Ahmad, Hastanto Januar Mantoro, Teddy Yulivan, Ivan State Intelligence College Medical Intelligence Postgraduate Program Bogor Indonesia Sampoerna University and State Intelligence College Media-Tech Lab Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9781665453899

Technological advances in the field of digitalization and networks of information have made the world enter the industrial revolution 4.0 including in the health and medical sector. This revolution is marked by the easy exchange of information in a short amount of time without being limited by geographical constraints and the shift from paper-based documents to digital. The health sector has also begun to adopt the 4.0 revolution by making health services more accessible to the public such as online medical applications, the use of electronic medical records, and the use of big data to personalize medical therapy for sick people. Even though the use of this information technology has a lot of benefits, there is also potential to be misused such as the purchase of dangerous medicines and leakage of patient's medical data also inadequate support from telemedical services due to a lack of guidance for personnel which has the potential to harm both physically and materially. Indonesia currently does not have a defense mechanism and regulations to protect its citizen from the misuse of information technology in the health industry. Systematic efforts are needed so that the use of information technology can run properly and safely. There need to be some measures to prevent the unlawful use of health information technology such as strict regulation and monitoring, defense against cyber hacking and leakage, also adequate training for the operators of health services in using the technology © 2022 IEEE.

关键词： medical applications

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Molecular architecture of mammalian pyruvate dehydrogenase complex

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Protein & Cell 2025年第1期16卷 72-78页

作者： Maofei Chen Yutong Song Sensen Zhang Yitang Zhang Xudong Chen Minghui Zhang Meng Han Xin Gao Sai Li Maojun Yang Tsinghua-Peking Center for Life Sciences Beijing Frontier Research Center for Biological StructureSchool of Life SciencesTsinghua UniversityBeijing 100084China Ministry of Education Key Laboratory of Protein Science Tsinghua UniversityBeijing 100084China State Key Laboratory of Membrane Biology Tsinghua UniversityBeijing 100084China Department of Biochemistry and Molecular Biology School of Basic Medical SciencesShenzhen University Health Science CenterShenzhen 518060China Protein Research Technology Center Protein Chemistry and Omics PlatformSchool of Life SciencesTsinghua UniversityBeijing 100084China Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Kingdom of Saudi Arabia Cryo-EM Facility Center Southern University of Science and TechnologyShenzhen 518055China Beijing Life Science Academy Beijing 102209China

Dear Editor,Pyruvate dehydrogenase complex(PDHc) is a large multienzyme assembly(Mr = 4–10 million Daltons) consisting of three essential components: pyruvate dehydrogenase(E1p), dihydrolipoyl transacetylase(E2p), and dihydrolipoyl dehydrogenase(E3). These three enzymes perform distinct functions sequentially to catalyze the oxidative decarboxylation of pyruvate with formation of nicotinamide adenine dinucleotide(NADH) and acetyl-coenzyme A(Patel and Roche, 1990).

关键词： sequentially oxidative Dalton

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Behavior Profiling of Role-Playing Game Players Based on Heuristic Event Log Mining 25th

Behavior Profiling of Role-Playing Game Players Based on Heu...

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25th International computer Symposium on New Trends in computer Technologies and Applications, ICS 2022

作者： Lin, Jason Tsai, Chia-Wei Yang, Chun-Wei Lafortune, Elie Department of Computer Science and Engineering National Chung Hsing University Taichung40227 Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung404 Taiwan Master Program for Digital Health Innovation College of Humanities and Sciences China Medical University Taichung406040 Taiwan

ISBN: (纸本)9789811995811

In this paper, we propose a novel player behavior model called the action priority model (APM) for representing player action behaviors. A play log is stored based on the game grammar under analysis, and heuristic filtering rules are applied to eliminate uninteresting events. The proposed model uses sequences and tendencies of event types as the main mining criteria to extract significant player action behaviors. Action sequences reflect the interdependencies of different actions and how a player may use their insights about the game rules to form a (hidden) action priority list (APL) with respect to action types. By mining for significant events from play logs, we aim to produce the APM and APL for different players. In experiments, the fuzzy miner tool was used to analyze a few event logs for the World of Warcraft game to obtain some noticeable discoveries about the structural and timing patterns of effective players. Experimental results show that two players in the same class with similar contributions to encounters will have a similar mined APM and may not have a minimal delay in the timing of actions. Players with a high contribution to an encounter will have an APM like a simulated optimal player along with large thickness edges in the APM to indicate a significant routine of actions. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Causal net Fuzzy miner Player behavior

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