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Interpreting Industrial IoT data Streams Through Fuzzy Querying With Hysteretic Fuzzy Sets on Apache Kafka

IEEE Transactions on Fuzzy Systems 2024年第8期32卷 4671-4684页

作者： Bożena Małysiak-Mrozek Bartłomiej Ryba Marek Molęda Che-Lun Hung Witold Pedrycz Weiping Ding Dariusz Mrozek Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland Department of Applied Informatics Silesian University of Technology Gliwice Poland Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei City Taiwan Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Department of Measurements and Control Systems Silesian University of Technology Gliwice Poland Research Center of Performance and Productivity Analysis Istinye University Istanbul Türkiye School of Artificial Intelligence and Computer Science Nantong University Nantong China Faculty of Data Science City University of Macau Macau China

In industrial settings, querying data streams from Internet of Things (IoT) devices benefits from utilizing elastic criteria to enhance the interpretability of the current state of the monitored environment. Fuzzy sets provide this elasticity, enabling the aggregation and representation of similar values in a human-comprehensible manner. However, many sensor signals exhibit temporal oscillations, leading to varying interpretations of the signal based on its current trend (rising or falling). This hysteresis in signal (and subsequently of the production device) interpretation inspired us to introduce this phenomenon into data stream processing, resulting in the novel concept of hysteretic fuzzy sets. This article demonstrates how fuzzy searching and grouping can be applied to IoT sensor signals in flexible Big data stream processing on Apache Kafka. We illustrate the impact of data stream querying with KSQL queries involving fuzzy sets (encompassing fuzzy filtering of data stream events, fuzzy transformation of data stream attributes, fuzzy grouping, and joining) on the flexibility of executed operations and computational resources utilized by the Kafka processing engine. Finally, our experiments with hysteretic fuzzy sets while analyzing sensor signals in power plants demonstrate that this novel approach effectively reduces the number of alarms while monitoring the state of the production machine.

关键词： Streams Fuzzy sets Internet of Things Big data Monitoring Production Real-time systems

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Influence of pre-adaptation time to background illumination on photopic negative response of the full-field electroretinogram

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ACTA OPHTHALMOLOGICA 2022年第S267期100卷

作者： Blum, Maren-Christina Klee, Sascha Institute of Biomedical Engineering and Informatics Technische Universität Ilmenau Ilmenau Germany Department of General Health Studies Division Biostatistics and Data Science Karl Landsteiner University of Health Sciences Krems Austria

Purpose The photopic negative response (PhNR) is a negative wave (latency ~70ms) of the full-field electroretinogram (ERG) which represents ganglion cell activity. The amplitude is most distinctive for a red stimulus on a blue background. In the literature different pre-adaptation times (range from few seconds to 10min) to the blue background can be found. The aim of the study presented here was to verify whether the pre-adaptation time to the blue background has an influence on the PhNR wave. Methods In total 12 subjects (25.4±2.0years, 4m, 8f) participated in the study. For visual stimulation, a full-field stimulator with a red flash (wavelength: 625nm, flash strength: 1.5cds/m², duration: ≤ 5ms, inter-flash interval: 1s) on blue background (wavelength: 455nm, luminance: 10cd/m²) was used. For ERG recording, Ag/AgCl ring-shaped skin-electrodes were placed at the lower eyelid, the ipsilateral earlobe, and the forehead. The subjects were light adapted before all measurements. Each ERG measurement consisted of a pre-adaptation time (15s/1/2/5/10min, randomized order) followed by an ERG measurement, which includes 20 artifact-free sweeps. The PhNR wave was defined as the minimum after the b-wave in the latency range from 60 to 80ms and was measured from the baseline (BT) and from the b-wave peak (PT). For statistical analysis, the Friedman-test was applied between the pre-adaptation times for both measured amplitudes and the latency (α=0.05). Results The PhNR could be measured and analyzed from all subjects. The Friedman-test showed no significant difference between the pre-adaption times for both amplitudes and the latency (*p ≤ 0.0167; p BT-amplitude = 0.281, p PT-amplitude = 0.797, p latency = 0.448). Conclusions We found no influence of the pre-adaption time to the blue background on the PhNR amplitude and latency. Therefore, for future work only a short pre-adaptation time of a few seconds is sufficient for a stable PhNR wave measurement for light adapted subject

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Incidence of 12 common cardiovascular diseases and subsequent mortality risk in the general population (Jun, zwad192, 2023)

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EUROPEAN JOURNAL OF PREVENTIVE CARDIOLOGY 2023年第15期30卷 1723-1723页

作者： Prugger, Christof Institute of Public Health Charité—Universitätsmedizin Berlin Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin Berlin Germany INSERM U970 Paris Cardiovascular Research Centre (PARCC) Integrative Epidemiology of Cardiovascular Diseases Université Paris Cité 56 rue Leblanc 75015 Paris France Institute of Health Informatics University College London 222 Euston Road NW1 2DA London UK Health Data Research UK 215 Euston Road NW1 2DA London UK UCL Hospitals Biomedical Research Centers (BRC) 270 Tottenham Court Road 215 Euston Road NW1 2BE London UK British Heart Foundation Data Science Center 215 Euston Road NW1 2BE London UK

Background Incident events of cardiovascular diseases (CVDs) are heterogenous and may result in different mortality risks. Such evidence may help inform patient and physician decisions in CVD prevention and risk factor *** This study aimed to determine the extent to which incident events of common CVD show heterogeneous associations with subsequent mortality risk in the general *** and results Based on England-wide linked electronic health records, we established a cohort of 1 310 518 people ≥30 years of age initially free of CVD and followed up for non-fatal events of 12 common CVD and cause-specific mortality. The 12 CVDs were considered as time-varying exposures in Cox’s proportional hazards models to estimate hazard rate ratios (HRRs) with 95% confidence intervals (CIs). Over the median follow-up of 4.2 years (2010–16), 81 516 non-fatal CVD, 10 906 cardiovascular deaths, and 40 843 non-cardiovascular deaths occurred. All 12 CVDs were associated with increased risk of cardiovascular mortality, with HRR (95% CI) ranging from 1.67 (1.47–1.89) for stable angina to 7.85 (6.62–9.31) for haemorrhagic stroke. All 12 CVDs were also associated with increased non-cardiovascular and all-cause mortality risk but to a lesser extent: HRR (95% CI) ranged from 1.10 (1.00–1.22) to 4.55 (4.03–5.13) and from 1.24 (1.13–1.35) to 4.92 (4.44–5.46) for transient ischaemic attack and sudden cardiac arrest, *** Incident events of 12 common CVD show significant adverse and markedly differential associations with subsequent cardiovascular, non-cardiovascular, and all-cause mortality risk in the general population.

关键词： Coronary heart disease Stroke Epidemiology Incidence Prevention Survival analysis

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The importance of contextual understanding in solving the last mile problem

The importance of contextual understanding in solving the la...

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作者： Kuziemsky, Craig Nøhr, Christian Marcilly, Romaric Dusseljee-Peute, Linda Zhu, Xinxin Elkin, Peter L. MacEwan University EdmontonAB Canada Danish Centre for Health Informatics Aalborg University Denmark Univ. Lille CHU Lille ULR 2694-METRICS: Evaluation des Technologies de Sante et des Pratiques Medicales LilleF-59000 France Department Medical Informatics University of Amsterdam Amsterdam University Medical Center Amsterdam Netherlands Center for Biomedical Data Science Yale University New HavenCT United States Department of Biomedical Informatics University at Buffalo United States

ISBN: (纸本)9781643682303

The COVID-19 pandemic has disrupted many global industries and shifted the digital health landscape by stimulating and accelerating the delivery of digital care. It has emphasized the need for a system level informatics implementation that supports the healthcare management of populations at a macro level while also providing the necessary support for front line care delivery at a micro level. From data dashboard to Telemedicine, this crisis has necessitated the need for health informatics transformation that can bridge time and space to provide timely care. However, heath transformation cannot solely rely on Health Information Technology (HIT) for progress, but rather success must be an outcome of system design focus on the contextual complexity of the health system where HIT is used. This conference highlights the important roles context plays for health informatics in global pandemics and aims to answer critical questions in four main areas: 1) health information management in the covid-19 context, 2) implementation of new practices and technologies in healthcare, 3) sociotechnical analysis of task performance and workload in healthcare, and 4) innovations in design and evaluation methods of health technologies. We deem this as a call to action to understand the importance of context while solving the last mile problem in delivering the informatics solutions that are needed to support our public health response. © 2021 The authors and IOS Press. All rights reserved.

关键词： Medical informatics

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An Overview and Recent Developments in the Analysis of Multistate Processes

arXiv

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arXiv 2025年

作者： Gorfine, Malka Cook, Richard J. Andersen, Per Kragh Therneau, Terry M. Joly, Pierre Putter, Hein Perme, Maja Pohar Abrahamowicz, Michal Department of Statistics and Operations Research Tel Aviv University Israel Department of Statistics and Actuarial Science University of Waterloo Canada Section of Biostatistics University of Copenhagen Denmark Division of Biomedical Statistics and Informatics Mayo Clinic New York United States Inserm ISPED Bordeaux Populations Health Research Center University of Bordeaux France Department of Biomedical Data Sciences Leiden University Medical Center Leiden Netherlands Department of Biostatistics and Medical Informatics Medical Faculty University of Ljubljana Slovenia Department of Epidemiology Biostatistics and Occupational Health McGill University MontrealQC Canada

Multistate models offer a powerful framework for studying disease processes and can be used to formulate intensity-based and more descriptive marginal regression models. They also represent a natural foundation for the construction of joint models for disease processes and dynamic marker processes, as well as joint models incorporating random censoring and intermittent observation times. This article reviews the ways multistate models can be formed and fitted to life history data. Recent work on pseudo-values and the incorporation of random effects to model dependence on the process history and between-process heterogeneity are also discussed. The software available to facilitate such analyses is listed. © 2025, CC BY.

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Comparative efficacy of NIBS and Physical Exercise on cognitive function in patients with MCI or AD: a systematic review and meta-analysis

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Med-X 2025年第1期3卷 1-29页

作者： Jiang, Yi Guo, Zhiwei Zhou, Xiaobo He, Jiayuan Wang, Yanyan Jiang, Ning National Clinical Research Center for Geriatrics West China Hospital Sichuan University Chengdu China West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China Center for Computational Systems Medicine McWilliams School of Biomedical Informatics The University of Texas Health Science Center at Houston Houston USA McGovern Medical School The University of Texas Health Science Center at Houston Houston USA West China School of Nursing National Clinical Research Center for Geriatrics West China Hospital Sichuan University/Healthcare Innovation Research Laboratory Nursing Key West China Hospital Sichuan University Chengdu China

Non-invasive brain stimulation (NIBS) and physical exercise (PE) intervention are currently the main and promising non-pharmacologic therapies for Alzheimer’s Disease (AD) or Mild cognitive impairment (MCI), but it is not clear which one is the most effective. Therefore, the aim was to compare the effectiveness of NIBS and PE interventions on cognitive function in MCI/AD, which can further elucidate their advantages and disadvantages in cognitive efficacy and facilitate the optimization of treatment strategies based on the specific cognitive status of patients. Randomized controlled trials (RCTs) were searched from online databases until December 2023. Standardized Mean Changes pre- and post-treatment were calculated for cognitive outcomes measures. 79 RCTs met the inclusion criteria. For the global cognitive scale, PE significantly affected AD and MCI. Both NIBS and PE had a sustained and significant impact on AD/MCI based on the follow-up. In sub-category functions, NIBS had a significant effect on memory for both AD and MCI. However, PE only had a significant effect on AD, not on MCI. For executive function, only NIBS had a significant effect on AD. For language, NIBS and PE both had a significant impact only on AD. For attention, the only significant effect was NIBS on AD. NIBS significantly affects more cognitive domains of AD than PE, and significantly improves the memory function of MCI. Given the current evidence, NIBS appears to be a more promising intervention approach for delaying cognitive decline in patients with MCI or AD compared to PE. • The current study is the first meta-analysis to compare the comparative efficacy of NIBS and PE interventions on various cognitive domains, for MCI or AD patients. • NIBS intervention has clear positive effect on various cognitive domains for patients with AD, and significant effect on the memory function for MCI. • NIBS is promising to be a better intervention approach to delay cognitive decline in MCI and AD than P

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Neural approximation of graph topological features 22

Neural approximation of graph topological features

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Zuoyu Yan Tengfei Ma Liangcai Gao Zhi Tang Yusu Wang Chao Chen Wangxuan Institute of Computer Technology Peking University IBM T. J. Watson Research Center Halicioglu Data Science Institute University of California Department of Biomedical Informatics Stony Brook University

ISBN: (纸本)9781713871088

Topological features based on persistent homology can capture high-order structural information which can then be used to augment graph neural network methods. However, computing extended persistent homology summaries remains slow for large and dense graphs and can be a serious bottleneck for the learning pipeline. Inspired by recent success in neural algorithmic reasoning, we propose a novel graph neural network to estimate extended persistence diagrams (EPDs) on graphs efficiently. Our model is built on algorithmic insights, and benefits from better supervision and closer alignment with the EPD computation algorithm. We validate our method with convincing empirical results on approximating EPDs and downstream graph representation learning tasks. Our method is also efficient; on large and dense graphs, we accelerate the computation by nearly 100 times.

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Heuristic-Based Extraction and Unigram Analysis of Nursing Free Text data Residing in Large EHR Clinical Notes 17th

Heuristic-Based Extraction and Unigram Analysis of Nursing...

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17th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2023

作者： Bokhari, Syed Mohtashim Abbas Krstovski, Kriste Withall, Jennifer Lee, Rachel Dykes, Patricia Tran, Mai Cato, Kenrick Rossetti, Sarah Department of Biomedical Informatics Columbia University New YorkNY United States Data Science Institute Columbia University New YorkNY United States Columbia Business School Columbia University New YorkNY United States School of Nursing Columbia University New YorkNY United States Harvard Medical School Brigham and Women’s Hospital BostonMA United States BWH Center for Patient Safety Research and Practice BostonMA United States University of Pennsylvania PhiladelphiaPA United States Children’s Hospital of Philadelphia PhiladelphiaPA United States

ISBN: (纸本)9783031597169

Free text in nurses’ notes can play an important role in clinical decision-making;however, such information has not been explored to the fullest of its potential as it is hard to extract it from electronic health records (EHRs). Free text is a subset of the information recorded in nursing notes. Automated extraction of free text is challenging due to EHRs’ size and structural diversity. Understanding these structural and content-level differences is essential for the extraction. Free text is embedded in other relatively structured texts, which are difficult to detect automatically. Moreover, there is no information indicating whether a note is a free text. As a first step in automating the extraction process, we explore heuristic-based algorithms with the goal of establishing a baseline and developing an annotated dataset, which could then be used for further machine learning-based extraction algorithms for a more scalable solution. In this research, we analyze over 200,000 EHR notes and extract 40,000 free text notes from them. Furthermore, we use the unigram language model to analyze the differences between free and structured texts to better understand the free text content. © ICST Institute for Computer sciences, Social informatics and Telecommunications Engineering 2024.

关键词： Nursing

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CFGNet: Coarse-to-Fine Grained Network for Image Classification of Confusing Fly Species

CFGNet: Coarse-to-Fine Grained Network for Image Classificat...

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Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： Lin Niu Bo Wang Baomin Niu Jin Zhang Danning Liang Jingpeng Wu Rong Zhou Jianqiang Deng School of Biomedical Informatics and Engineering Hainan Medical University Haikou China Hainan Vocational College of Political Science and Law Hainan Gongping Forensic Center Haikou China School of Big Data and Artificial Intelligence Zhengzhou University of Science and Technology Zhengzhou China Center for Educational Technology Hainan Medical University Haikou China Department of Forensic Medicine of Hainan Medical University Haikou China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

In the realm of fine-grained image classification (FGIC), despite substantial technological advancements, models frequently misclassify certain fly species with exceedingly high inter-class similarity, thereby impacting overall classification accuracy. To address this challenge, we propose an innovative Coarse-to-Fine Grained Network Framework (CFGNet). This architecture tackles the issue through two stages: Initially, to alleviate classification complexity, closely related and easily confused categories are amalgamated. Subsequently, a lightweight coarse-grained information extraction network (CM) is introduced to enhance the classification accuracy of samples with relatively larger class differences. In the second stage, for precise identification of challenging samples, a fine-grained feature extraction network (FM) is proposed exclusively for difficult-to-distinguish images. Additionally, we introduce a novel feature reuse method to deeply mine and repurpose coarse-grained features from the previous stage to guide fine-grained classification and improve performance in this stage. To validate our approach, we curated a dataset based on merged confusion classes annotation (Flies-MCC). Experimental results demonstrate that our proposed network architecture achieves significant advancements in fine-grained fly classification and offers a pioneering solution for precise fly identification.

关键词： Accuracy Frequency modulation Annotations Network architecture Information retrieval Feature extraction Pattern recognition Complexity theory Artificial intelligence Image classification

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Investigating the Contribution of Coding Variants in Alcohol Use Disorder Using Whole-Exome Sequencing Across Ancestries

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Biological Psychiatry 2025年第1期98卷 46-55页

作者： Wang, Lu Kranzler, Henry R. Gelernter, Joel Zhou, Hang Department of Psychiatry Yale University School of Medicine New Haven Connecticut United States Veterans Affairs Connecticut Healthcare System West Haven CT United States Department of Psychiatry University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania United States Crescenz Veterans Affairs Medical Center Philadelphia Pennsylvania United States Department of Genetics Yale School of Medicine New Haven Connecticut United States Department of Neuroscience Yale School of Medicine New Haven Connecticut United States Department of Biomedical Informatics and Data Science Yale School of Medicine New Haven Connecticut United States Center for Brain and Mind Health Yale School of Medicine New Haven Connecticut United States

Background: Alcohol use disorder (AUD) is a leading cause of death and disability worldwide. There has been substantial progress in identifying genetic variants that underlie AUD. However, whole-exome sequencing studies of AUD have been hampered by the lack of available samples. Methods: We analyzed whole-exome sequencing data of 4530 samples from the Yale-Penn cohort and 469,835 samples from the UK Biobank, which represent an unprecedented resource for exploring the contribution of coding variants in AUD. After quality control, 1750 African-ancestry (1142 cases) and 2039 European-ancestry (1420 cases) samples from the Yale-Penn and 6142 African-ancestry (130 cases), 415,617 European-ancestry (12,861‬ cases), and 4607 South Asian (130 cases) samples from the UK Biobank cohorts were included in the analyses. Results: We confirmed the well-known functional variant rs1229984 in ADH1B (p = 4.88 × 10⁻³¹) and several other variants in ADH1C. Gene-based collapsing tests that considered the high allelic heterogeneity revealed the previously unreported genes CNST (p = 1.19 × 10⁻⁶), attributable to rare variants with allele frequency < 0.001, and IFIT5 (p = 3.74 × 10⁻⁶), driven by the burden of both common and rare loss-of-function and missense variants. Conclusions: This study extends our understanding of the genetic architecture of AUD by providing insights into the contribution of rare coding variants, separately and convergently with common variants in AUD. © 2025 Society of Biological Psychiatry

关键词： Alcohol use disorder Coding variants Multiancestry Rare variants Substance use disorder Whole-exome sequencing

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