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58th Hawaii International Conference on System Sciences, HICSS 2025

作者： da Silva, Rayssa Nascimento de Mello, Ana Carolina Nobre Serra, Cristiano Barroso Garcia, Ana Cristina Bicharra UNIRIO Graduate Program on Informatics (PPGI) Brazil

ISBN: (纸本)9780998133188

This study investigates the artificial intelligence (AI) impact on hospital admission classification, specifically focusing on the identification of patients discharged from the emergency room who required readmission within seven days. The research aims to scrutinize potential inconsistencies in human classification within medical triage records and understand the disparities between human decisions and AI predictions. Class balancing techniques like Class Weights and K-Fold Cross-Validation (AI model) and SMOTE, Random Oversampling (The Random Forest) were employed to address imbalanced data, improving the model's ability to generalize across different patient groups. Following the delimitation of cancer cases, the dataset was refined to 7,525 rows, involving 1,945 unique patients. After machine learning training for screening and hospital admission, 80 cases (4.11%) of inconsistent data were observed in triage records, highlighting the critical importance of accurate classification in preventing unnecessary hospital readmissions. The study analyzed key features influencing predictions using the Random Forest algorithm, providing insights to enhance patient management and reduce mortality. The Random Forest classifier exhibited a median accuracy of 72.79% (Mean Precision: 0.76%, Mean Recall: 0.84%, AUC Score: 0.76% and Mean F1-Score: 0.80%), while the neural network showed a Mean Accuracy of 70.3% (Mean Precision: 0.80, Mean Recall: 0.71, AUC Score: 0.75 and Mean F1-Score: 0.75), indicating consistent performance. Feature importance analysis emphasized vital signs, showcasing AI's capability to identify inconsistencies and the need for evolution in triage practices. © 2025 IEEE Computer Society. All rights reserved.

关键词： Machine Learning Oncology Patients Readmission Prediction Risk Stratification Vital Signs

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Convergence analysis of a Schrödinger problem with moving boundary

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Mathematics and Computers in Simulation 2025年 238卷 45-64页

作者： Alfaro Vigo, Daniel G. Gomes, Daniele C.R. do Carmo, Bruno A. Rincon, Mauro A. Institute of Computing Federal University of Rio de Janeiro Caixa Postal 68.530 RJ Rio de JaneiroCEP 21941-590 Brazil Graduate Program in Informatics Federal University of Rio de Janeiro Caixa Postal 68.530 RJ Rio de JaneiroCEP 21941-590 Brazil

In this article, we present the mathematical analysis of the convergence of the linearized Crank–Nicolson Galerkin method for a nonlinear Schrödinger problem related to a domain with a moving boundary. The convergence analysis of the numerical method is carried out for both semidiscrete and fully discrete problems. We establish an optimal error estimate in the L2-norm with order O(τ2+hs),2≤s≤r, where h is the finite element mesh size, τ is the time step, and r−1 represents the degree of the finite element polynomial basis. Numerical simulations are provided to confirm the consistency between theoretical and numerical results, validating the method and the order of convergence for different degrees p≥1 of the Lagrange polynomials and also for Hermite polynomials (degree p=3), which form the basis of the approximate solution. © 2025 International Association for Mathematics and Computers in Simulation (IMACS)

关键词： Convergence of numerical methods

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M-Health Solution to Support the Monitoring of Elderly Patients After Hospital Discharge 15th

M-Health Solution to Support the Monitoring of Elderly Patie...

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15th International Symposium on Ambient Intelligence, ISAmI 2024

作者： Cazella, Silvio César Dihl, Fernanda Viegas Stein, Airton Tetelbom Machado, Leticia Rocha Graduate Program in Information Technologies and Health Management Federal University of Health Sciences of Porto Alegre Sarmento Leite St. 245 RS Porto Alegre90050-170 Brazil Graduate Program in Informatics in Education Federal University of Rio Grande do Sul Paulo Gama Av. 110 RS Porto Alegre90040-060 Brazil

ISBN: (纸本)9783031831164

Digital health can assist health professionals in monitoring elderly individuals after hospital discharge using technology, techniques, and practices that provide environments conducive to autonomy, comfort, and safety for already vulnerable patients. This article aims to describe the process of modeling and designing an m-Health solution that includes features for monitoring elderly patients post-discharge. The method applied was a case study, analyzing the reality of a hospital institution in Porto Alegre, Rio Grande do Sul, Brazil. The target audience for this study comprised elderly individuals with preserved cognitive functions and health professionals involved in the hospital discharge process. The modeling of the m-Health solution prototype followed the User-Centered Design method. Data collection instruments included questionnaires, interviews, and observations focusing on the two target groups. As a result, we developed a prototype, characterized by an m-Health solution with relevant functions to facilitate post-hospital discharge management. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： mHealth

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Enhanced Performance and Data Privacy in Lung Nodule Classification via Federated Deep Learning Approach 24

Enhanced Performance and Data Privacy in Lung Nodule Classif...

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2024 7th International Conference on Healthcare Service Management, ICHSM 2024

作者： Nguyen, Duc-Khanh Li, Ai-Hsien Adam Lai, Yen-Jun Chiu, Yen-Ling Phan, Dinh-Van Chien, Ting-Ying Chan, Chien-Lung Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Statistics and Informatics University of Economics The University of Danang Danang Viet Nam Division of Cardiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan ZDT Group- YZU Joint Research and Development Center for Big Data Taoyuan Taiwan Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400710162

This study explores the feasibility of deep learning for classifying nodule neoplasms, analyzing their performance on two openly available datasets, LUNGx SPIE, and LIDC-IDRI. These datasets offer valuable diversity in image quantity, quality, and malignancy annotation methodologies, posing a real-world challenge for effective model development. To handle the disparities in these diverse datasets, we aim to develop a robust classification model trained using federated learning. This decentralized approach allows model training across datasets without sharing patient data, preserving privacy and overcoming data fragmentation. Our experiments demonstrate that federated learning enables the development of a robust and superior deep learning model compared to individual models trained on limited data. This combination of deep learning, and federated learning holds immense promise for nodule neoplasm classification and can significantly improve patient care and outcomes. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Exploring Parallelism in ZynqNet AI Engine to Accelerate Image Classification in Aerospace

Exploring Parallelism in ZynqNet AI Engine to Accelerate Ima...

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IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Eduardo Marañon Aguilar Fabio Benevenuti Fernanda Lima Kastensmidt Federal University of Rio Grande do Sul (UFRGS) Institute of Informatics Microelectronics Graduate Program (PGMICRO) Porto Alegre Brazil

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

This research investigates various configurations of the Zynqnet AI Engine to accelerate image classification tasks using the SAT-6 dataset and a 4-layer CNN. The C++ code for the hardware accelerator was translated into RTL using Vivado HLS and integrated with the Zynq Processing System on a ZedBoard Zynq-7000 ARM/FPGA SoC. The AI engine was configured to work with 32 -bit floating point, 8bit fixed point and 8 -bit integer type operations, with different processing elements for parallelizing CNN tasks. Given a 64-bit data memory bus in communication with the accelerator and an 8-bit integer quantization, the variation in the number of processing elements does not result in a significant performance improvement. To address this, 8 values were grouped for reading and writing in 64-bit parallel mode, and different communication architectures were designed to improve data transfer between the accelerator and DDR memory, with optional enablement of L1D and L2 cache. The research analyzes CNN computation performance and FPGA resource usage (LUTs, FFs, BRAMs, DSPs) across configurations, providing insights into the trade-offs in FPGA-based CNN implementations, aiding the development of efficient, high-performance hardware accelerators for Deep Learning applications.

关键词： Quantization (signal) Writing Table lookup System-on-chip Convolutional neural networks Artificial intelligence Hardware acceleration Engines Field programmable gate arrays Image classification

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Impact of Balancing and Regularization on the Semantic Segmentation of Gleason Patterns

Impact of Balancing and Regularization on the Semantic Segme...

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18th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Paraíso, Eduardo Henrique S. Machado, Alexei M. C. PPGInf - Graduate Program on Informatics Pontifical Catholic University of Minas Gerais Dom José Gaspar 500 Belo Horizonte Brazil Department of Anatomy and Imaging Universidade Federal de Minas Gerais Alfredo Balena 190 Belo Horizonte Brazil

This study investigates the impact of class balancing and regularization on improving the diagnostic agreement in prostate histological images. The U-Net models applied to the Prostate Cancer Grade Assessment dataset reveal that class balancing combined with traditional loss functions contributes to an increase of up to 6 percentage points in image agreement. Combining balancing and Focal Loss can increase image classification agreement by an average of 13 percentage points compared to using an imbalanced dataset with traditional loss functions. Notably, distinguishing between Gleason patterns 3 and 4 in medical image analysis is crucial, as this distinction not only directly influences clinical decisions and the prognosis of prostate cancer patients but also emphasizes the need for careful interpretation of the data. © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

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Innovative web-based microteaching model: To improve the teaching skills of prospective informatics teachers in vocational high schools

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Social Sciences and Humanities Open 2025年 11卷

作者： Sugihartini, Nyoman Elmunsyah, Hakkun Nurhadi, Didik Rahmawati, Yuni Vocational Education Doctoral Program Post Graduate School Universitas Negeri Malang Malang Indonesia Informatics Education Study Program Department of Informatics Faculty of Engineering and Vocational Universitas Pendidikan Ganesha Bali Indonesia

Prospective informatics teachers in vocational schools (VS) must master content knowledge, pedagogical skills, and technology integration to achieve learning goals. Therefore, informatics teachers in VS need to innovate to increase students' creativity. However, many prospective informatics teachers experience difficulties in this regard. This research aims to develop and implement the Web-Based Microteaching (WBM) model to improve the teaching skills of prospective informatics teachers at VS. This study uses research and development methods with the ADDIE model approach. The research results show that the WBM model effectively improves prospective informatics teachers' teaching skills, especially in planning lessons and practising basic teaching skills. This innovation is a relevant learning solution in the digital era and positively contributes to the professional development of informatics teachers. The WBM model is an innovation to provide a more intensive and structured practical experience by utilizing web technology as the main media. Through web-based learning, prospective teachers can access material, exercises and feedback independently, which is expected to accelerate pedagogical mastery. © 2025 The Authors

关键词： Microteaching model Prospective informatics teacher Teaching skills Vocational high schools Web-based

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Design and Evaluation of an Event-Driven Cloud-Based Architecture for a Remote Patient Monitoring System

Design and Evaluation of an Event-Driven Cloud-Based Archite...

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IEEE International Conference on Software Architecture Workshops (ICSAW)

作者： Pedro Fiuza Linhares Pedro H. Gaya Wanderley Marza de Sousa Zaranza Maria Andréia Formico Rodrigues Nabor C. Mendonça Post Graduate Program in Applied Informatics University of Fortaleza Fortaleza CE Brazil Center of Technological Sciences University of Fortaleza Fortaleza CE Brazil Intensive Care Unit Hospital Instituto Dr. José Frota (IJF) Fortaleza CE Brazil Post Graduate Program in Applied Informatics/GIRA Lab University of Fortaleza Fortaleza CE Brazil

ISBN: (数字)9798331533366

ISBN: (纸本)9798331533373

This paper presents an event-driven cloud-based serverless architecture for a remote patient monitoring system. The architecture is designed to be scalable, resilient, and cost-effective, and leverages managed cloud services to handle load variations and service faults. To evaluate the architecture, we conducted comprehensive capacity, scalability, and resilience tests. The test results show that the architecture can meet the performance, scalability, and reliability requirements of a remote patient monitoring system. Additionally, a detailed analysis of operational costs in the cloud confirms the cost-effectiveness of the solution and highlights practical opportunities for improvements.

关键词： Cloud computing Patient monitoring Software architecture Scalability Computer architecture Load management Performance analysis Reliability Resilience Testing

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The association between urinary tract infections and diet: a literature review

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Discover Medicine 2025年第1期2卷 1-17页

作者： Çelik, Haluk Kozan, Esra Caf, Balım Bengisu Çebi, Gizem Koç, Murat Vivosens Inc. San Francisco USA Program of Stem Cell and Tissue Engineering Institute of Graduate Education Istinye University Istanbul Türkiye Department of Health Informatics University of Health Science Hamidiye Institute of Health Sciences Istanbul Türkiye

Urinary tract infections (UTIs) are a significant global health concern, affecting millions worldwide, with particular prevalence among specific vulnerable populations. Women of sexually active age, post-menopausal women, pregnant women, individuals with diabetes, catheterized patients, childs and older indivisuals face heightened risk due to various physiological, hormonal, and anatomical factors. While anatomical abnormalities and behavioral factors contribute to UTI susceptibility, the growing challenge of antibiotic resistance has necessitated exploration of alternative treatment approaches. Non-antibiotic interventions, including probiotics, cranberry products, and natural remedies, are gaining attention for their potential role in UTI prevention and management. Recent studies suggest the efficacy of probiotics and plant extracts in promoting urinary tract health and preventing bacterial colonization. Additionally, research highlights the importance of dietary factors, such as vitamin D and probiotic intake, in reducing UTI susceptibility. This review examines the emerging shift toward non-drug strategies, offering a more comprehensive approach to UTI management while addressing the pressing concern of antibiotic resistance.

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SwiftMedSAM: An Ultra-lightweight Prompt-Based Universal Medical Image Segmentation Model for Highly Constrained Environments

SwiftMedSAM: An Ultra-lightweight Prompt-Based Universal Med...

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International Challenge on Segment Anything in Medical Images on Laptop held in conjunction with the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024

作者： Kong, Youngbin Kim, Kwangtai Jeong, Seoi Lee, Kyu Eun Kong, Hyoun-Joong Interdisciplinary Program in Bioengineering Graduate School Seoul National University Seoul Korea Republic of Department of Transdisciplinary Medicine Seoul National University Hospital Seoul Korea Republic of Department of Surgery Seoul National University Hospital and College of Medicine Seoul Korea Republic of Department of Medicine Seoul National University College of Medicine Seoul Korea Republic of Innovative Medical Technology Research Institute Seoul National University Hospital Seoul Korea Republic of Interdisciplinary Program of Medical Informatics Seoul National University College of Medicine Seoul Korea Republic of

ISBN: (纸本)9783031818530

Medical image segmentation is a crucial step for accurate diagnosis and treatment planning, as it provides quantitative information about anatomical structures and pathological lesions in various clinical scenarios. However, the existing methodologies have limitations in terms of their generalizability and computational efficiency. In this study, we propose SwiftMedSAM, an ultra-lightweight prompt-based general model, to enable efficient medical image segmentation even in resource-constrained environments. Based on LiteMedSAM, we significantly reduced the model size and computational complexity through the hyperparameter optimization of the image encoder and mask decoder components. SwiftMedSAM showed remarkable performance across various imaging modalities enabling real-time inference in resource-limited computing environments. It achieved a validation score of 0.75 demonstrating that SwiftMedSAM outperformed the existing methodologies in terms of the trade-off between accuracy and efficiency. Owing to its unprecedented generalizability and low computational cost, SwiftMedSAM is expected to enable high-quality medical image analysis in resource-constrained settings, thereby contributing to advancements in precision medicine and telemedicine. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Medical imaging

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