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2019 International Conference on computer science, information technology, and Electrical Engineering, ICOMITEE 2019

作者： Mulyadi, Ely Aryanto, Kadek Yota Ernanda Candiasa, I. Made Politeknik Negeri Jember Department of Information Technology Jember Indonesia Universitas Pendidikan Ganesha Department of Computer Science of Postgraduate Program Singaraja Indonesia

ISBN: (纸本)9781728134369

The aim purposed of this research is to evaluate clinical management information system called (E-CLINIC) which is integrated with the Primary Care (Pcare) application provided by Indonesia sosial healt insurance organization called BPJS Kesehatan to first level of health cares facility called as FKTP. This evaluation is intended to measure the maturity level on used of two integrated applications using COBIT 4.1 framework. The integration of the Clinic's management information system with PCare is considered to be the main resource that has strategic value to be able to manage information effectively and efficiently for the achievement of organizational goals. The results achieved have been running in accordance with the organization's business objectives, this can be seen from the level of maturity that reaches level 3 (Defined), which is a condition in which the organization has formal and written standard procedures that have been socialized to all levels of management and employees to be obeyed and run in daily activities. © 2019 IEEE.

关键词： information management

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Interactive 3D Segmentation for Primary Gross Tumor Volume in Oropharyngeal Cancer

arXiv

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arXiv 2024年

作者： Saukkoriipi, Mikko Sahlsten, Jaakko Jaskari, Joel Orsmaa, Lotta Kangas, Jari Rasouli, Nastaran Raisamo, Roope Hirvonen, Jussi Mehtonen, Helena Järnstedt, Jorma Mäkitie, Antti Naser, Mohamed Fuller, Clifton Kann, Benjamin Kaski, Kimmo Department of Computer Science Aalto University School of Science Espoo Finland Faculty of Information Technology and Communication Sciences Computing Sciences University of Tampere Tampere Finland Department of Radiology Tampere University Faculty of Medicine and Health Technology Tampere University Hospital Tampere Finland Department of Otorhinolaryngology-Head and Neck Surgery Research Program in Systems Oncology Faculty of Medicine University of Helsinki Helsinki University Hospital Helsinki Finland Department of Radiation Oncology The University of Texas MD Anderson Cancer Center HoustonTX United States Artificial Intelligence in Medicine Program Mass General Brigham Harvard Medical School BostonMA United States Department of Radiation Oncology Dana-Farber Cancer Institute Brigham and Women's Hospital Harvard Medical School BostonMA United States The Alan Turing Institute British Library 96 Euston Rd LondonNW1 2DB United Kingdom

The main treatment modality for oropharyngeal cancer (OPC) is radiotherapy, where accurate segmentation of the primary gross tumor volume (GTVp) is essential. However, accurate GTVp segmentation is challenging due to significant interobserver variability and the time-consuming nature of manual annotation, while fully automated methods can occasionally fail. An interactive deep learning (DL) model offers the advantage of automatic high-performance segmentation with the flexibility for user correction when necessary. In this study, we examine interactive DL for GTVp segmentation in OPC. We implement state-of-the-art algorithms and propose a novel two-stage Interactive Click Refinement (2S-ICR) framework. Using the 2021 HEad and neCK TumOR (HECKTOR) dataset for development and an external dataset from The University of Texas MD Anderson Cancer Center for evaluation, the 2S-ICR framework achieves a Dice similarity coefficient of 0.713 ± 0.152 without user interaction and 0.824 ± 0.099 after five interactions, outperforming existing methods in both cases. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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REVOLUTIONIZING RADIOTHERAPY COMMUNICATION SKILLS TRAINING: AN INNOVATIVE CHATBOT-BASED PROTOTYPE APPROACH (SCIMORT)

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Journal of Medical Imaging and Radiation sciences 2023年第3期54卷 S14-S15页

作者： Aniza, Azmi Nor Afiqah, Mohd Sukri Nur Ikhwan, Mustafa Kamal Muhamad Rozilawati, Ahmad Azmie, Yahya Noorazrul Safwan, Ahmad Fadzil Muhammad Adilah, Suhaimi Nani Noraini, Ahmad Wahid Izzuan, Hussin Saiful Liyana, Shuib Diagnostic Imaging and Radiotherapy Program Faculty of Health Sciences Universiti Kebangsaan Malaysia 50300 Kuala Lumpur Malaysia Department of Mathematical Sciences Faculty of Science and Technology Universiti Kebangsaan Malaysia 43600 Selangor Darul Ehsan Malaysia Department of Information Systems Faculty of Computer Science and Information Technology University of Malaya 50603 Kuala Lumpur Malaysia

OBJECTIVEThis study aimed to develop and evaluate the effectiveness of a chatbot-based prototype model called SCIMORT, designed to improve the communication skills of undergraduate students in Diagnostic Imaging and Radiotherapy at Universiti Kebangsaan Malaysia. MATERIALS & METHODSThis pilot and survey study involved the development of the SCIMORT module, which included the creation of a 3D virtual patient with visual and auditory cues. The SCIMORT module was evaluated by surveying 28 participants from the PDR cohorts. The questionnaire was designed to assess user acceptance and engagement of SCIMORT, and the expert validation of the questionnaire was assessed using Content Validity Ratio and Cronbach Alpha Coefficient. RESULTSA total of 28 participants from the PDR cohorts were enrolled in these projects. In the first phase, the virtual patient developed for SCIMORT exhibited visual cues such as blinking and eye movement, as well as auditory cues, including verbal responses and intonation of voice. During the second phase, expert validation of the questionnaire yielded favourable RESULTS: , with a Content Validity Ratio exceeding 83% and a Cronbach Alpha Coefficient surpassing 70%. However, both the control group (users' acceptance = 3.919 ± 1.245; users' engagement = 3.526 ± 1.270) and the Trial group (users' acceptance = 3.953 ± 1.114; users' engagement = 3.568 ± 1.142) showed neutral outcomes. CONCLUSIONThe SCIMORT module proved to be an effective tool for pre-clinical learning of communication skills in radiotherapy. However, the control and the Trial groups exhibited neutral results regarding users' acceptance and engagement. Further improvements are needed to make the module more interactive and adaptable to student needs.

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A computational framework for longitudinal medication adherence prediction in breast cancer survivors: A social cognitive theory based approach

arXiv

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arXiv 2025年

作者： Kaur, Navreet Gonzales, Manuel Alcaraz, Cristian Garcia Gong, Jiaqi Wells, Kristen J. Barnes, Laura E. Department of Systems and Information Engineering University of Virginia CharlottesvilleVA United States SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology San DiegoCA United States Department of Computer Science The University of Alabama TuscaloosaAL United States Department of Psychology San Diego State University San DiegoCA United States

Non-adherence to medications is a critical concern since nearly half of patients with chronic illnesses do not follow their prescribed medication regimens, leading to increased mortality, costs, and preventable human distress. Amongst stage 0-3 breast cancer survivors, adherence to long-term adjuvant endocrine therapy (i.e., Tamoxifen and aromatase inhibitors) is associated with a significant increase in recurrence-free survival. This work aims to develop multi-scale models of medication adherence to understand the significance of different factors influencing adherence across varying time frames. We introduce a computational framework guided by Social Cognitive Theory for multi-scale (daily and weekly) modeling of longitudinal medication adherence. Our models employ both dynamic medication-taking patterns in the recent past (dynamic factors) as well as less frequently changing factors (static factors) for adherence prediction. Additionally, we assess the significance of various factors in influencing adherence behavior across different time scales. Our models outperform traditional machine learning counterparts in both daily and weekly tasks in terms of both accuracy and specificity. Daily models achieved an accuracy of 87.25% (Precision – 92.04%, Recall – 93.15%, Specificity – 77.50%), and weekly models, an accuracy of 76.04% (Precision – 75.83%, Recall – 85.80%, Specificity – 72.30%). Notably, dynamic past medication-taking patterns prove most valuable for predicting daily adherence, while a combination of dynamic and static factors is significant for macro-level weekly adherence patterns. While our models exhibit strong predictive performance, they are constrained by potential cohort-specific biases, reliance on self-reported adherence data, and a limited understanding of the context around non-adherence. Future research will focus on external validation across diverse populations and explore the real-world implementation of sensor-rich systems for a more compre

关键词： Endocrinology

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Precision Oral Medicine: A DPR Segmentation and Transfer Learning Approach for Detecting Third Molar Compress Inferior Alveolar Nerve

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IEEE Journal of Translational Engineering in Health and Medicine 2025年

作者： Lin, Yuan-Jin Chen, Shih-Lun Mao, Yi-Cheng Chen, Tsung-Yi Peng, Cheng-Hao Tsai, Tzu-Hsiang Li, Kuo-Chen Chen, Chiung-An Tu, Wei-Chen Abu, Patricia Angela R. National Cheng Kung University Department of Program on Semiconductor Manufacturing Technology Academy of Innovative Semiconductor and Sustainable Manufacturing Tainan City701401 Taiwan Chung Yuan Christian University Department of Electronic Engineering Taoyuan City32023 Taiwan Taoyuan Chang Gang Memorial Hospital Department of Operative Dentistry Taoyuan City33305 Taiwan Feng Chia University Department of Electronic Engineering Taichung city40724 Taiwan Chung Yuan Christian University Taoyuan City32023 Taiwan Chung Yuan Christian University Department of Information Management Taoyuan City32023 Taiwan Ming Chi University of Technology Department of Electronic Engineering New Taipei City243303 Taiwan National Cheng Kung University Department of Electrical Engineering Tainan city701401 Taiwan Ateneo de Manila University Department of Information Systems and Computer Science Quezon City Philippines

Objective: Extraction of the third molar of the mandible is one of the most common oral surgical procedures. Preoperative monitoring and assessment are crucial to mitigate neurological risks. Identifying whether the third molar in the mandible compresses the inferior alveolar nerve still relies on dental professionals, a task that is repetitive and time-consuming. Thus, the primary objective is to utilize dental panoramic radiography for image processing and classify whether the third molar compresses the inferior alveolar nerve, aiming to reduce the demand for CT images in symptom diagnosis and mitigate the risks associated with high-dose radiation. technology or Method: This study proposes an innovative dental panoramic radiography segmentation technique to locate the third molar position. Subsequently, an innovative edge masking enhancement method is used to extract features of the inferior alveolar nerve and the third molar. Moreover, a transformer-based image detection model to consider whether the third molar compresses the inferior alveolar nerve. Results: The third molar position localization method achieved an accuracy rate of 97.92%, compared to recent research at least improved by 3.6% accuracy. Subsequently, innovative edge masking and image enhancement methods improve classification accuracy by 4.3%, when supplemented with computed tomography scan images for further evaluation, the maximum accuracy reached 98.45%, representing a 4.5% improvement compared to previous studies. Conclusions: The third molar position detection results will impact the identification of the inferior alveolar nerve compressed by the third molar. Through the innovative edge region segmentation algorithm can effectively distinguish this object, and the overall evaluation accuracy can be improved by approximately 3.8%. © 2013 IEEE.

关键词： Neurophysiology

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CNN-based cardiac motion extraction to generate deformable geometric left ventricle myocardial models from Cine MRI

arXiv

引用

arXiv 2021年

作者： Upendra, Roshan Reddy Wentz, Brian Jamison Simon, Richard Shontz, Suzanne M. Linte, Cristian A. Center for Imaging Science Rochester Institute of Technology RochesterNY United States Biomedical Engineering Rochester Institute of Technology RochesterNY United States Bioengineering Graduate Program University of Kansas LawrenceKS United States Electrical Engineering and Computer Science University of Kansas LawrenceKS United States Information and Telecommunication Technology Center University of Kansas LawrenceKS United States

Patient-specific left ventricle (LV) myocardial models have the potential to be used in a variety of clinical scenarios for improved diagnosis and treatment plans. Cine cardiac magnetic resonance (MR) imaging provides high resolution images to reconstruct patient-specific geometric models of the LV myocardium. With the advent of deep learning, accurate segmentation of cardiac chambers from cine cardiac MR images and unsupervised learning for image registration for cardiac motion estimation on a large number of image datasets is attainable. Here, we propose a deep leaning-based framework for the development of patient-specific geometric models of LV myocardium from cine cardiac MR images, using the Automated Cardiac Diagnosis Challenge (ACDC) dataset. We use the deformation field estimated from the VoxelMorph-based convolutional neural network (CNN) to propagate the isosurface mesh and volume mesh of the end-diastole (ED) frame to the subsequent frames of the cardiac cycle. We assess the CNN-based propagated models against segmented models at each cardiac phase, as well as models propagated using another traditional nonrigid image registration technique. © 2021, CC BY.

关键词： Image registration

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Cryptanalysis and improvement of the measurement-device-independent quantum key distribution with hyper-encoding

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Modern Physics Letters A 2022年第31期37卷

作者： Lin, Jason Tsai, Chia-Wei Yang, Chun-Wei Department of Computer Science and Engineering National Chung Hsing University No. 145 Xingda Rd. South District Taichung 40227 Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology No. 129 Sec. 3 Sanmin Rd. North District Taichung 40401 Taiwan Master Program for Digital Health Innovation College of Humanities and Sciences China Medical University No. 100 Sec. 1 Jingmao Rd. Beitun District Taichung 406040 Taiwan

Recently, Cui et al. [Sci. China Phys. 62, 110311 (2019)] proposed a measurement-device-independent quantum key distribution (MDI-QKD) scheme based on high-dimensional encoding to increase the channel capacity of each individual photon. A secret key is shared among two legitimate parties via the help of a dishonest third-party (TP). They claimed that the proposed protocol can improve the drawback of the low-key generation rate that existed in the past MDI-QKD schemes. However, this study pointed out a loophole during the security check of TP. That is, the two legitimate participants merely use the corresponding qubit pairs that are both generated in the diagonal basis for the discussion, which leads to a potential attack that not only allows TP to avoid the security check but also creates partial inconsistency of key bits for both communicating sides. Fortunately, this problem can be fixed by a minor correction of the protocol. © World Scientific Publishing Company.

关键词： dishonest third party Measurement-device-independent quantum cryptography quantum key distribution

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Modeling the number of confirmed cases of COVID-19 in East Java using negative binomial regression based on least square spline estimator

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AIP Conference Proceedings 2024年第1期3083卷

作者： Arip Ramadan Nur Chamidah I. Nyoman Budiantara Student of Doctoral Study Program of Mathematics and Natural Sciences Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia. Department of Information System School of Industrial and System Engineering Telkom University Surabaya 60231 Indonesia. Department of Mathematics Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia. Research Group of Statistical Modeling in Life Science Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia. Department of Statistics Faculty of Sciences and Data Analytics Sepuluh Nopember Institute of Technology Surabaya 60111 Indonesia.

The East Java Province has experienced a significant surge in number of confirmed cases of COVID-19. This study endeavors to investigate the potential correlation between weather conditions and the incidence of number of confirmed cases of COVID-19 in East Java. To achieve this, a nonparametric regression model, specifically, the Negative Binomial Regression (NBR) model based on the least squares spline estimator, was utilized. The outcomes of the study indicate that the Mean Absolute Percentage Error (MAPE) of nonparametic regression model is 0.30. Meanwhile, the MAPE for the parametric regression model is 0.34. The results show that a nonparametric regression model approach is better than parametric regression model approach. The study establishes that the truncated spline estimator based NBR model represents the best fit, with an MLCV value of -256.71. The findings of the study suggest that a temperature less than 21.75°C is associated with a decrease of 13.28 number of confirmed cases of COVID-19 per each 1°C increase, while a temperature between 21.75°C and 25.78°C is linked to an increase of 6.85 number of confirmed cases of COVID-19 per each 1°C increase. In contrast, a temperature greater than 25.78°C is associated with a decrease of 139.42 number of confirmed cases of COVID-19 per each 1°C increase. Similarly, a wind speed less than 5.57 m/s is related to a decrease of 12.99 number of confirmed cases of COVID-19 per each 1 m/s increase, whereas a wind speed between 5.57 m/s and 8.99 m/s is associated with a decrease of 10.29 number of confirmed cases of COVID-19 per each 1 m/s increase. Furthermore, a wind speed greater than 8.99 m/s is linked to a decrease of 19.16 number of confirmed cases of COVID-19 per each 1 m/s increase. The study provides evidence that higher temperatures and wind speeds result in a slower rise in the incidence of the number of confirmed cases of COVID-19. Consequently, it is recommended that the local government remains vigilant du

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Enhancing safety with an AI-empowered assessment and monitoring system for BSL-3 facilities

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Heliyon 2025年第1期11卷 e40855页

作者： Fan, Yi-Ling Hsu, Ching-Han Wu, Ju-Yu Tsai, Ying-Ying Chen, Wei J. Lee, Min-Shi Hsu, Fang-Rong Liao, Lun-De Institute of Biomedical Engineering and Nanomedicine National Health Research Institutes 35 Keyan Road Miaoli County Zhunan Town 350 Taiwan Department of Biomedical Engineering & Environmental Sciences National Tsing-Hua University Hsinchu Taiwan Doctoral Program in Tissue Engineering and Regenerative Medicine National Chung Hsing University Taichung Taiwan Center for Neuropsychiatric Research National Health Research Institutes 35 Keyan Road Miaoli County Zhunan Town 350 Taiwan National Institute of Infectious Diseases and Vaccinology National Health Research Institutes 35 Keyan Road Miaoli County Zhunan Town 350 Taiwan Department of Information Engineering and Computer Science Feng Chia University Taichung 407 Taiwan

Introduction: The COVID-19 pandemic has created an urgent demand for research, which has spurred the development of enhanced biosafety protocols in biosafety level (BSL)-3 laboratories to safeguard against the risks associated with handling highly contagious pathogens. Laboratory management failures can pose significant hazards. Methods: An external system captured images of personnel entering a laboratory, which were then analyzed by an AI-based system to verify their compliance with personal protective equipment (PPE) regulations, thereby introducing an additional layer of protection. A deep learning model was trained to detect the presence of essential PPE items, such as clothing, masks, hoods, double-layer gloves, shoe covers, and respirators, ensuring adherence to World Health Organization (WHO) standards. The internal laboratory management system used a deep learning model to delineate alert zones and monitor compliance with the imposed safety protocols. Results: The external detection system was trained on a dataset consisting of 4112 images divided into 15 PPE compliance classes. The model achieved an accuracy of 97.52 % and a recall of 97.03 %. The identification results were presented in real time via a visual interface and simultaneously stored on the administrator's dashboard for future reference. We trained the internal management system on 3347 images, achieving 90 % accuracy and 85 % recall. The results were transmitted in JSON format to the internal monitoring system, which triggered alerts in response to violations of safe practices or alert zones. Real-time notifications were sent to the administrators when the safety thresholds were met. Conclusion: The BSL-3 laboratory monitoring system significantly reduces the risk of exposure to pathogens for personnel during laboratory operations. By ensuring the correct use of PPE and enhancing adherence to the imposed safety protocols, this system contributes to maintaining the integrity of BSL-3 facilities

关键词： Bioengineering Biomedical instrumentation Biomedical laboratory safety Biosafety level-3 laboratory

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The Effect of Several Kernel Functions to Financial Transaction Anomaly Detection Performance using One-Class SVM

The Effect of Several Kernel Functions to Financial Transact...

引用

2019 International Congress on Applied information technology, AIT 2019

作者： Heryadi, Yaya Dandalina, D. Bina Nusantara University Computer Science Department Jakarta11530 Indonesia Master of Information and Technology Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781728158624

Anomalies in data become ubiquitous and often unavoidable. Despite it might be caused by various errors during the data collection or transportation, some anomalies potentially give important information such as indication of a new underlying process. The aim of anomaly detection task is to determine all such instances in the input dataset in a data-driven fashion. In the past decade, the proliferation of anomaly data in many domains have raised research interest resulted in a plethora of anomaly detection methods A vast number of previous study reports suggested that there is no single model will achieve the best performance for every dataset. This paper presents empiric results on the effect of several kernel functions to performance of One-Class Support Vector Machine (OC-SVM) as an anomaly detector model. The proposed model is tested using financial transactions of microfinance service dataset from an Indonesian Bank. The empiric results showed that OC-SVM model with Sigmoid and RBF kernels achieve the best statistically significant value of training, validation, and testing accuracies than the OC-SVM model with no-kernel, linear kernel and polynomial (degree 3,4,5,6) kernels. © 2019 IEEE.

关键词： Anomaly detection

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