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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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An automated parameter optimizer for data transfer performance testing

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Software Impacts 2025年 24卷

作者： Yun, Daqing Zuo, Liudong Gu, Yi Wu, Chase Computer and Information Sciences Program Harrisburg University Harrisburg 17101 PA United States Department of Computer Science California State University Dominguez Hills Carson 90747 CA United States Department of Computer Science Middle Tennessee State University Murfreesboro 37132 TN United States Department of Data Science New Jersey Institute of Technology Newark 07102 NJ United States

This work presents an automated tool for optimizing control parameters in performance testing of big data transfer over long-fat network connections. Supporting both TCP and UDT protocols, the tool identifies the optimal configurations to enhance the efficiency of large-scale data transfers. A stochastic approximation algorithm is employed for parameter optimization, streamlining the protocol and parameter selection. The tool has been evaluated in various network scenarios, including long-haul connections in real-world high-performance networks. Its modular design also enables straightforward integration of additional data transfer protocols and alternative optimization methods. © 2025 The Author(s)

关键词： Big data transfer High-speed networks Network testing Parameter optimization Stochastic approximation

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In silico prediction of loop-mediated isothermal amplification using a generalized linear model

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Synthetic Biology 2025年第1期10卷

作者： Taguchi, Kenshiro Michiyuki, Satoru Tsuji, Takumasa Kotoku, Jun’ichi Graduate Degree Program of Health Data Science Teikyo University 2-11-1 Kaga Itabashi-Ku Tokyo173-8605 Japan Fundamental Research Laboratory Eiken Chemical Co. Ltd Tochigi329-0114 Japan Graduate School of Medical Care and Technology Teikyo University 2-11-1 Kaga Itabashi-Ku Tokyo173-8605 Japan

Loop-mediated isothermal amplification (LAMP), a DNA amplification technique under isothermal conditions, provides the important benefits of high sensitivity, specificity, rapidity, and simplicity. Maximizing LAMP features necessitates the design of a complex LAMP primer set (LPS) consisting of four primers for six regions of a given target DNA. Furthermore, the LPS of a given target DNA is designed with LPS design support software such as Primer Explorer. However, even if the design is completed, we still must do many in vitro experiments and evaluations. Consequently, designing LPS often fails to achieve high performance, including efficient amplification. For this study, we examined in silico LAMP: a generalized linear model to predict DNA amplification from LPS. Using logistic regression with elastic net regularization, we identified factors that strongly affect LPS design. These factors, combined with domain knowledge for LPS design, led to the creation of LAMP kernel variables that are highly essential for high LAMP reaction. In silico LAMP, constructed using logistic regression with LAMP kernel variables, allows classification and performance prediction of LPS with an area under the curve of 0.86. These results suggest that a high LAMP reaction can be predicted using LAMP kernel variables and generalized linear regression model. Moreover, an LPS with high performance can be constructed without in vitro experimentation. © The Author(s) 2025. Published by Oxford University Press.

关键词： Logistic regression

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Endemism shapes viral ecology and evolution in globally distributed hydrothermal vent ecosystems

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Nature Communications 2025年第1期16卷 1-14页

作者： Langwig, Marguerite V. Koester, Faith Martin, Cody Zhou, Zhichao Joye, Samantha B. Reysenbach, Anna-Louise Anantharaman, Karthik Department of Bacteriology University of Wisconsin-Madison Madison WI United States Freshwater and Marine Sciences Program University of Wisconsin-Madison Madison WI United States Microbiology Doctoral Training Program University of Wisconsin-Madison Madison WI United States Department of Marine Sciences University of Georgia Athens GA United States Department of Biology Portland State University Portland OR United States Department of Integrative Biology University of Wisconsin-Madison Madison WI United States Department of Data Science and AI Wadhwani School of Data Science and AI Indian Institute of Technology Madras TN Chennai India

Viruses are ubiquitous in deep-sea hydrothermal vents, where they influence microbial communities and biogeochemistry. Yet, viral ecology and evolution remain understudied in these environments. Here, we identify 49,962 viruses from 52 globally distributed hydrothermal vent samples (10 plume, 40 deposit, and 2 diffuse flow metagenomes), and reconstruct 5708 viral metagenome-assembled genomes, the majority of which were bacteriophages. Hydrothermal viruses were largely endemic, however, some viruses were shared between geographically separated vents, predominantly between the Lau Basin and Brothers Volcano in the Pacific Ocean. Geographically distant viruses shared proteins related to core functions such as structural proteins, and rarely, proteins of auxiliary functions involved in processes such as fermentation and cobalamin biosynthesis. Common microbial hosts of viruses included members of Campylobacterota, Alpha-, and Gammaproteobacteria in deposits, and Gammaproteobacteria in plumes. Campylobacterota- and Gammaproteobacteria-infecting viruses reflected variations in hydrothermal chemistry and functional redundancy in their predicted microbial hosts, suggesting that hydrothermal geology is a driver of viral ecology and coevolution of viruses and hosts. Our results indicate that viral ecology and evolution in globally distributed hydrothermal vents is shaped by endemism and thus may have increased susceptibility to the negative impacts of deep-sea mining and anthropogenic change in ocean ecosystems. © The Author(s) 2025.

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Development of location suitability prediction for health facilities using random forest machine learning in 2030 integrating remote sensing and GIS in West Java, Indonesia

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Environmental Advances 2025年 19卷

作者： Virtriana, Riantini Ihsan, Kalingga Titon Nur Anggraini, Tania Septi Deliar, Albertus Harto, Agung Budi Riqqi, Akhmad Sakti, Anjar Dimara Remote Sensing and GIS Research Group Faculty of Earth Science and Technology Bandung Institute of Technology Bandung 40132 Indonesia Center for Remote Sensing Bandung Institute of Technology Bandung 40132 Indonesia Doctor Program in Program Study of Geodesy and Geomatic Engineering Faculty of Earth Sciences and Technology Bandung 40132 Indonesia Geographic Information Science Universitas Pendidikan Indonesia Bandung 40111 Indonesia Center for Spatial Data Infrastructure Bandung Institute of Technology Bandung 40132 Indonesia

Access to healthcare facilities is crucial in the present day. Healthcare facilities must be proportional to the population in a given area. Therefore, it is important to increase the number of healthcare facilities in regions where there is an imbalance. Proper planning and a sustainability review are necessary when determining locations for healthcare facilities. Environmental changes can affect the suitability of a location in the future. Thus, planning that can predict future suitability conditions is required to ensure that the built locations have high sustainability. This study predicts the suitability of healthcare facility locations in 2030 in West Java using remote sensing and Geographic Information System (GIS). Both static and dynamic data processed at 30×30 meter intervals across West Java will be used. Geospatial and remote sensing data are utilized in the study. Dynamic parameter extrapolation uses data from 2000 to 2018. The random forest machine learning method is employed to obtain the suitability values for healthcare facility locations using existing health facility training data in West Java. The results show changes in the suitability classes of healthcare facilities in each region from 2018 to 2030, with some areas experiencing an increase or decrease in class. This research highlights consistently suitable locations, ensuring their sustainability as healthcare facility sites. © 2024

关键词： GIS Health Facility Machine Learning Remote Sensing

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Bayesian curriculum generation in sparse reward reinforcement learning environments

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Engineering science and Technology, an International Journal 2025年 66卷

作者： Akgün, Onur Üre, N. Kemal Istanbul Technical University Graduate School Mechatronics Engineering Program Maslak Istanbul 34469 Turkey Turkish-German University Faculty of Engineering Mechatronics Engineering Department Beykoz Istanbul 34820 Turkey Istanbul Technical University Faculty of Computer and Informatics Engineering Artificial Intelligence and Data Engineering Department Maslak Istanbul 34469 Turkey Istanbul Technical University Artificial Intelligence and Data Science Research Center Maslak Istanbul 34469 Turkey

This paper introduces the Bayesian Curriculum Generation Algorithm, a sophisticated approach for curriculum learning in sparse reward reinforcement learning contexts. Diverging from traditional methodologies, this algorithm utilizes Bayesian networks to dynamically create tasks by altering problem parameters, thereby impacting task difficulty. It operates independently from the core reinforcement learning algorithm, enabling compatibility with a variety of RL techniques. A notable feature of our algorithm is its capability for unsupervised task classification, utilizing a clustering process applicable to both image outputs and scalar values. This method efficiently categorizes tasks based on difficulty, circumventing the need for exhaustive training for each task. However, the effectiveness of this approach is contingent upon the presence of definable parameters within the environment and necessitates domain expertise to determine the appropriate tool, be it image output or scalar parameter analysis. The algorithm selects tasks from a curated pool corresponding to specific difficulty levels and adapts according to the agent's performance. Successful task completion triggers the generation of more complex tasks, whereas encountering challenges results in the maintenance or minor adjustment of task complexity. This adaptive feature significantly enhances the efficiency of the learning process. Empirical evaluations conducted in various environments, characterized by maze-like structures, discrete or continuous settings, and the presence of adversarial entities hindering the agent's mission, demonstrate the algorithm's efficacy and its superiority over conventional methods. The Bayesian Curriculum Generation Algorithm represents a significant advancement in reinforcement learning, providing a dynamic and adaptable solution for complex learning challenges. © 2025 The Authors

关键词： Bayesian networks Curriculum learning Reinforcement learning Unsupervised clustering

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Improved LSTM hyperparameters alongside sentiment walk-forward validation for time series prediction

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Journal of Open Innovation: Technology, Market, and Complexity 2025年第1期11卷

作者： Wahyuddin, Eko Putra Caraka, Rezzy Eko Kurniawan, Robert Caesarendra, Wahyu Gio, Prana Ugiana Pardamean, Bens Department of Statistical Computing Politeknik Statistika STIS Jakarta 13330 Indonesia Statistics Indonesia (BPS) Jl. Dr Sutomo 6-8 Jakarta Indonesia School of Economics and Business Telkom University Bandung 40257 Indonesia Research Center for Data and Information Sciences Research Organization for Electronics and Informatics National Research and Innovation Agency (BRIN) Bandung 40135 Indonesia Faculty of Integrated Technologies Universiti Brunei Darussalam Gadong BE1410 Brunei Darussalam Department of Mathematics Universitas Sumatera Utara Medan 20155 Indonesia Bioinformatics and Data Science Research Centre Bina Nusantara University DKI Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University DKI Jakarta 11480 Indonesia

This study aims to address the common issue of biased estimation errors in time series modeling by analyzing the error in locating ideal hyperparameters and defining appropriate validation methods. Specifically, it focuses on predicting the stock price of Bank Rakyat Indonesia using a combination of historical stock prices, technical indicators, exchange rates, and news sentiment data, while determining the optimal variables for deep learning models. Employing a deep learning-based Long-Short Term Memory (LSTM) model, the study optimizes hyperparameters alongside walk-forward validation for time series prediction. It explores different combinations of variables and adapts the sliding window approach to the context of the data. The results highlight the importance of optimizing hyperparameters and utilizing walk-forward validation for accurate time series prediction. The model incorporating historical stock prices and sentiment scores outperforms others, achieving an RMSE of 96.61 and MAE of 86.97. Incorporating sentiment scores reduces RMSE by 39.55 % compared to models using only historical stock prices, while adding technical indicators does not yield improvement. This study contributes to the field by addressing the issue of biased estimation errors in time series modeling, offering insights into the optimization of hyperparameters and validation methods for accurate predictions. It also underscores the significance of incorporating sentiment analysis alongside historical stock prices for improved forecasting accuracy. © 2024 The Authors

关键词： LSTM Sentiment analysis Stock price prediction Time series modeling Validation methods

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Hospital Transfers for Hip Fracture Patients Are Associated With Higher Cost and Mortality

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Journal of Arthroplasty 2025年

作者： Hutchison, Catherine E.T. Rollier, Genevieve Li, Hsin-Fang Wells, Erica Workman, Kimberly L. Duwelius, Paul J. Lieberman, Elizabeth G. Oregon Health & Science University Department of Orthopaedics and Rehabilitation Portland Oregon United States Providence St. Joseph Health Surgery & Orthopedic Program Portland Oregon United States Providence St. Joseph Health Center for Cardiovascular Analytics Research & Data Science (CARDS) Portland Oregon United States Orthopedic and Fracture Specialists Portland Oregon United States

Background: Transferring hip fracture patients may increase morbidity and cost. The purpose of this study was to examine time to surgery, outcomes, and cost in patients undergoing operative management of displaced femoral neck fractures (DFNFs), comparing those admitted through the emergency department (ED) and those transferred from outside hospitals. Methods: A retrospective review of surgically managed DFNF patients (age ≥ 65 years) at a single institution between May 29, 2020, and December 29, 2022, was performed. Patients were categorized into two groups: “ED arrivals” and “transfers.” Demographics, time from presentation to surgery, hospital length of stay, discharge disposition, 1-year readmission, reoperation, and mortality, and total cost of care were collected and compared. We identified 459 patients (ED = 422, 92.0%, Transfer = 37, 8.0%). There was no difference in age, sex, body mass index, or American Society of Anesthesiologists status. Results: Median time from ED to operating room was higher in the transfer group (41.6 versus 23.2 hours, P < 0.0001). Transfer and ED patients had similar length of stay (5.9 versus 6.0 days, P = 0.29) and similar rates of discharge to skilled nursing facility (66.1 versus 62.2%, P = 0.39). There was a higher rate of 1-year mortality (27.0 versus 13.5% for ED, P = 0.03) in transfer patients but no difference in 1-year readmission or reoperation. Adjusting for demographics, transfer patients had 2.6 higher odds of 1-year mortality than ED patients (odds ratio = 2.59 (1.13 to 5.94) P = 0.03). The cost of care was higher for transfer patients ($23,521 versus $13,980, P < 0.0001). Conclusions: Transferring DFNF patients was associated with delayed time to surgery, increased 1-year mortality, and higher costs. Transferring patients is a logistical reality for many conditions;however, efforts should be made to provide efficient, safe, and cost-effective care. © 2025 Elsevier Inc.

关键词： femoral neck fracture geriatric fracture health care costs hip fracture transfer of care

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Variational Transformer Ansatz for the Density Operator of Steady States in Dissipative Quantum Many-Body Systems

arXiv

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arXiv 2025年

作者： Wei, Lu Jia, Zhian Wang, Yufeng Kaszlikowski, Dagomir Ling, Haibin Department of Applied Mathematics & Statistics Stony Brook University Stony BrookNY11794 United States Program of Data Science Stony Brook University Stony BrookNY11794 United States Centre for Quantum Technologies National University of Singapore SG 117543 Singapore Department of Physics National University of Singapore SG 117543 Singapore Department of Computer Science Stony Brook University Stony BrookNY11794 United States

The transformer architecture, known for capturing long-range dependencies and intricate patterns, has extended beyond natural language processing. Recently, it has attracted significant attention in quantum information and condensed matter physics. In this work, we propose the transformer density operator ansatz for determining the steady states of dissipative quantum many-body systems. By vectorizing the density operator as a many-body state in a doubled Hilbert space, the transformer encodes the amplitude and phase of the state’s coefficients, with its parameters serving as variational variables. Our design preserves translation invariance while leveraging attention mechanisms to capture diverse long-range correlations. We demonstrate the effectiveness of our approach by numerically calculating the steady states of dissipative Ising and Heisenberg spin chain models, showing that our method achieves excellent accuracy in predicting steady states. © 2025, CC BY.

关键词： Hilbert spaces

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Testing the Inference Accuracy of Accelerator-Friendly Approximate Phylogeny Tracking

Testing the Inference Accuracy of Accelerator-Friendly Appro...

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Computational Intelligence in Artificial Life and Cooperative Intelligent Systems (ALIFE-CIS), IEEE Symposium on

作者： Matthew Andres Moreno Anika Ranjan Emily Dolson Luis Zaman Department of Ecology and Evolutionary Biology University of Michigan Ann Arbor United States Center for the Study of Complex Systems University of Michigan Ann Arbor United States Michigan Institute for Data and AI in Society University of Michigan Ann Arbor United States Undergraduate Research Opportunities Program University of Michigan Ann Arbor United States Department of Computer Science and Engineering Michigan State University East Lansing United States Program in Ecology Evolution and Behavior Michigan State University East Lansing United States

ISBN: (数字)9798331508371

ISBN: (纸本)9798331508388

Computer simulations are an important tool for studying the mechanics of biological evolution. In particular, agent-based approaches provide an opportunity to collect high-quality records of ancestry relationships. Such phylogenies can provide insight into evolutionary dynamics within these simulations. Previous work generally tracks lineages directly, yielding an exact phylogenetic record of evolutionary history. However, challenges exist in scaling direct ancestrytracking approaches to highly-distributed, many-processor evolution in silico. An alternative approach is to estimate phylogenetic history via noncoding annotations on digital genomes, akin to how bioinformaticians build phylogenies by assessing genetic similarities between organisms. Recent work has extended this “hereditary stratigraphy” approach to support powerful hardware accelerator platforms, such as the Cerebras Wafer-Scale Engine. Although these second-generation “surface”-based hereditary stratigraphy algorithms have demonstrated order-ofmagnitude speedups over first-generation “column”-based algorithms, it remains unknown how they impact the accuracy of reconstructed phylogenies. To address this question, we assessed reconstruction accuracy under alternative configurations across a matrix of evolutionary conditions varying in selection pressure, spatial structure, and ecological dynamics. Encouragingly, we find that the second-generation approaches provide higher reconstruction quality across most surveyed conditions.

关键词： Surface reconstruction Accuracy Annotations Heuristic algorithms Computational modeling Biological system modeling Stratigraphy Phylogeny History Bars

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