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Prospective evaluation of data-driven models to predict daily risk of Clostridioides difficile infection at 2 large academic health centers (vol 2, pg 1, 2022)

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INFECTION CONTROL & HOSPITAL EPIDEMIOLOGY 2023年第10期44卷 1705-1705页

作者： Kamineni, Meghana Otles, Erkin Oh, Jeeheh Rao, Krishna Young, Vincent B. Li, Benjamin Y. West, Lauren R. Hooper, David C. Shenoy, Erica S. Guttag, John G. Wiens, Jenna Makar, Maggie Electrical Engineering and Computer Science Department Massachusetts Institute of Technology Cambridge Massachusetts Medical Scientist Training Program University of Michigan Medical School Ann Arbor Michigan Department of Industrial and Operations Engineering University of Michigan College of Engineering Ann Arbor Michigan Division of Computer Science and Engineering University of Michigan College of Engineering Ann Arbor Michigan Department of Internal Medicine Division of Infectious Diseases University of Michigan Medical School Ann Arbor Michigan Infection Control Unit Massachusetts General Hospital Boston Massachusetts Division of Infectious Diseases Massachusetts General Hospital Boston Massachusetts Harvard Medical School Boston Massachusetts

Many data-driven patient risk stratification models have not been evaluated prospectively. We performed and compared the prospective and retrospective evaluations of 2 Clostridioides difficile infection (CDI) risk-prediction models at 2 large academic health centers, and we discuss the models’ robustness to data-set shifts.

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Research on Deep Learning Model of Feature Extraction Based on Convolutional Neural Network 2

Research on Deep Learning Model of Feature Extraction Based ...

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2nd IEEE International Conference on Image Processing and computer Applications, ICIPCA 2024

作者： Liu, Houze Li, Iris Liang, Yaxin Sun, Dan Yang, Yining Yang, Haowei New York University Department Computer Science and Engineering New York United States Graduate School of Art & Science New York University New York United States Viterbi School of Engineering University of Southern California Los Angeles United States Washington University Department of Electrical and Systems Engineering St. Louis United States School of Computer Science Carnegie Mellon University Pittsburgh United States University of Houston Cullen College of Engineering Houston United States

ISBN: (纸本)9798350360240

Neural networks with relatively shallow layers and simple structures may have limited ability in accurately identifying pneumonia. In addition, deep neural networks also have a large demand for computing resources, which may cause convolutional neural networks to be unable to be implemented on terminals. Therefore, this paper will carry out the optimal classification of convolutional neural networks. Firstly, according to the characteristics of pneumonia images, AlexNet and InceptionV3 were selected to obtain better image recognition results. Combining the features of medical images, the forward neural network with deeper and more complex structure is learned. Finally, knowledge extraction technology is used to extract the obtained data into the AlexNet model to achieve the purpose of improving computing efficiency and reducing computing costs. The results showed that the prediction accuracy, specificity, and sensitivity of the trained AlexNet model increased by 4.25 percentage points, 7.85 percentage points, and 2.32 percentage points respectively. The graphics processing usage has decreased by 51% compared to the InceptionV3 mode. © 2024 IEEE.

关键词： Convolutional neural networks

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Experimental investigation of high-speed WDM-visible light communication using blue, green, and red InGaN µLEDs

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Optics Letters 2024年第16期49卷 4697-4700页

作者： Lu, Tingwei Dai, Yurong Lee, Tzu-Yi Wang, Shuhui Guo, Wenan Lai, Shouqiang Chen, Zhong Kuo, Hao-Chung Wu, Tingzhu National Integrated Circuit Industry and Education Integration Innovation Platform Department of Electronic Science Xiamen University Xiamen 361005 China College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Department of Photonics and Graduate Institute of Electro-Optical Engineering Hsinchu 30010 Taiwan Semiconductor Research Center Hon Hai Research Institute Taipei 11492 Taiwan

Micro-light-emitting diodes (µLEDs) hold significant promise for applications in displays and visible light communication (VLC). This study substantiates the viability of a wavelength division multiplexing (WDM)-VLC system using InGaN blue, green, and red µLED devices. The devices exhibited notable color stability and high modulation bandwidth due to the weakly polarized electric field in the blue and green semipolar devices and the stress-optimized structure in the red device. The aggregated data rate reached 11.14 Gbps. Moreover, the blue, green, and red InGaN µLEDs exhibited a wide color gamut, encompassing 119.4% of the NTSC and 89.2% of the Rec. 2020 standards, affirming the potential of blue, green, and red InGaN µLEDs for applications in full-color display and WDM-VLC systems. © 2024 Optica Publishing Group.

关键词： Atomic layer deposition Bandpass filters Crosstalk Scanning electron microscopy Visible light communications Wavelength division multiplexing

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Reachability Analysis of Nonlinear Discrete-Time Systems Using Polyhedral Relaxations and Constrained Zonotopes*

Reachability Analysis of Nonlinear Discrete-Time Systems Usi...

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IEEE Conference on Decision and Control

作者： Brenner S. Rego Guilherme V. Raffo Marco H. Terra Joseph K. Scott Department of Chemical and Biomolecular Engineering Georgia Institute of Technology 311 Ferst Dr. Atlanta GA USA Department of Electrical and Computer Engineering University of Säo Paulo Säo Carlos SP Brazil Department of Electronics Engineering and the Graduate Program in Electrical Engineering Federal University of Minas Gerais Belo Horizonte MG Brazil

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper presents a novel algorithm for reachability analysis of nonlinear discrete-time systems. The proposed method combines constrained zonotopes (CZs) with polyhedral relaxations of factorable representations of nonlinear functions to propagate CZs through nonlinear functions, which is normally done using conservative linearization techniques. The new propagation method provides better approximations than those resulting from linearization procedures, leading to significant improvements in the computation of reachable sets in comparison to other CZ methods from the literature. Numerical examples highlight the advantages of the proposed algorithm.

关键词： Discrete-time systems Linearization techniques Approximation algorithms Complexity theory Reachability analysis

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A Multi-phase Deep Learning Approach for Predicting HCC Response to TACE using Complete Computed Tomography

A Multi-phase Deep Learning Approach for Predicting HCC Resp...

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2023 IEEE EMBS Special Topic Conference on Data Science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Chang, Shun Cheng Liu, Chien Fu Misztal, Marta Hsieh, Yi Hsien Lin, Che Taiwan School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Electrical Engineering Ntu Taiwan Center for Computational and Systems Biology Center for Biotechnology Ntu Taiwan Smart Medicine and Health Informatics Program Ntu Taiwan

ISBN: (纸本)9798350383386

In this research study, we compare the predictive performance of two advanced deep learning-based models in order to provide a solution to TACE (Transarterial Chemoembolization) response prediction in HCC (Hepatocellular Carcinoma) patients. Using entire abdominal CT scans enabled a broader perspective available for the model, eliminating the need for segmentation during the preprocessing. Making use of both single-phase and multi-phase CT imaging, we have used DenseNet121 and have obtained an accuracy of 80% for the multi-phase *** Relevance: The ability to predict the effectiveness of TACE treatment prior to its administration makes it possible to provide a better decision-making aid for physicians and patients. © 2023 IEEE.

关键词： computerized tomography

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Manta Ray Foraging Optimization Approaches on Multivariable PID Controller Tuning

Manta Ray Foraging Optimization Approaches on Multivariable ...

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Congress on Evolutionary Computation

作者： Yan Lieven Souza Luúio Luiza Scapinello Aquino Da Silva Viviana Cocco Mariani Leandro Dos Santos Coelho Electrical Engineering Graduate Program (PPGEE) Federal University of Parana (UFPR) Curitiba Brazil Department of Electrical Engineering Mechanical Engineering Graduate Program (PPGEM) Pontifical Catholic University of Parana (PUCPR) and Federal University of Parana (UFPR) Curitiba Parana Brazil Department of Electrical Engineering Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) and Federal University of Parana (UFPR) Curitiba Brazil

This work presents a performance comparison between the newly developed Manta Ray Foraging Optimization (MRFO) and two different variants created for tuning a decentralized fractional order proportional-integral-derivative (FOPID) controller for a multiple-input multiple-output (MIMO) application. The application consists of a ball mill pulverizing system to pulverize coal and maximize fuel efficiency. MRFO and its applications have the task of finding the optimal controller variable values to control the system's temperature and pressure. The MRFO is a metaheuristic based on the behavior of manta rays, and although it shows efficient performance it may be improved through the use of techniques which generate new variants of the algorithm. Two techniques were used in the de-velopment of new variants: Quantum mechanics and opposition-based learning. The three different versions of MRFO were used to minimize a custom fitness function which is a combination of the integral time squared error (ITSE) and the overshoot of the system response. Simulations were carried using Simulink and Matlab softwares. For analyzing the performances, statistical measures such as minimum, maximum, best, mean, median, and standard deviation of the fitness function over 50 runs were used. Additionally, the different variants were also compared for minimizing 10 benchmark functions. The results show that the use of the previously mentioned techniques improve the performance of the original MRFO in optimizing the FOPID to control the ball mill pulverizing system.

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Performance Evaluation of HV Equipment in a Powerplant Switchyard for Life Extension, Practical Experience in Thailand 10

Performance Evaluation of HV Equipment in a Powerplant Switc...

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10th International Conference on Condition Monitoring and Diagnosis, CMD 2024

作者： Suwanasri, Thanapong Panmala, Nattapon Suwanasri, Cattareeya Thanasettagone, Thanachat Lacharochana, Sakda The Sirindhorn International Thai-German Graduate School of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Technical Education Department of Teacher Training in Electrical Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand Unipower Engineering Co. Ltd. Bangkok Thailand Global Power Synergy Public Company Limited Bangkok Thailand

ISBN: (纸本)9788986510225

in a 115 kV switchyard substation, there are several major equipment that are gradually deteriorated due to ageing and degradation. Those equipment are one of the important element in a power generating utility to supply the reliable electricity to diverse customers. Therefore, their failures would cause catastrophic consequences and lead to long outage time. Thus, the need for condition and performance assessments of those switchyard equipment for life extension purposes is obligatory to ensure system reliability, to meet customer requirements and to optimize the investment cost. To access the condition of such high voltage equipment, protective relays, building structure and equipment foundations, their technical specification as well as visual inspection and preventive maintenance results of individual equipment must be systematically analyzed to determine the possibility for life extension according to manufacturer guideline as well as international standards and recommendations for condition aging assessment representing in term of conditional aging index. While the other aspects as age, rating adequacy, technology obsolescence, satisfaction in operation, failure rate and defect severity are required to determine the need for renovation/replacement known as replacement index. Then, the proposed methodology has been employed to assess the condition and performance of each equipment such as every major high voltage equipment, control building, protective relays in a switchyard of power station with satisfactory result. © 2024 The Korean Institute of electrical Engineers (KIEE).

关键词： Obsolescence

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Joint Source Decomposition of Concurrent EEG-FMRI Data in Epilepsy and Control Groups

Joint Source Decomposition of Concurrent EEG-FMRI Data in Ep...

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IEEE International Symposium on Biomedical Imaging

作者： J. Mason Harding Haatef Pourmotabbed Yamin Li Kimberly Rogge-Obando Kate Wang Sarah E. Goodale Shiyu Wang Camden Bibro Bergen Allee Caroline Martin Victoria L. Morgan Dario J. Englot Catie Chang Department of Electrical and Computer Engineering Vanderbilt University Department of Biomedical Engineering Vanderbilt University Department of Computer Science Vanderbilt University Neuroscience Graduate Program Vanderbilt University Vanderbilt Memory and Alzheimer's Center Vanderbilt University Medical Center Department of Neurological Surgery Vanderbilt University Medical Center Department of Radiology Vanderbilt University Medical Center

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

EEG and fMRI are complementary, noninvasive technologies for investigating human brain function. These modalities have been used to uncover large-scale functional networks and their disruptions in clinical populations. Given the high temporal resolution of EEG and high spatial resolution of fMRI, integrating these modalities can provide a more holistic understanding of brain activity. This work explores a multimodal source decomposition technique for extracting shared modes of temporal variation between fMRI BOLD signals and EEG spectral power fluctuations in the resting state. The resulting components are then compared between patients with focal epilepsy and controls, revealing multimodal network differences between groups.

关键词： Neuroimaging Brain Fluctuations Epilepsy Functional magnetic resonance imaging Electroencephalography Pattern recognition Hemodynamics Spatial resolution Signal resolution

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Multi-modal Multitask Learning Model for Simultaneous Classification of Two Epilepsy Biomarkers 27th

Multi-modal Multitask Learning Model for Simultaneous Classi...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Broti, Nawara Mahmood Sawada, Masaki Takayama, Yutaro Iijima, Keiya Iwasaki, Masaki Ono, Yumie Electrical Engineering Program Graduate School of Science and Technology Meiji University Kanagawa Kawasaki Japan Department of Neurosurgery Yokohama City University Hospital Kanagawa Yokohama Japan Department of Neurosurgery National Center Hospital National Center of Neurology and Psychiatry Tokyo Kodaira Japan Department of Electronics and Bioinformatics School of Science and Technology Meiji University Kanagawa Kawasaki Japan

ISBN: (纸本)9783031781971

Accurate estimation of the epileptogenic zone is critical for surgical treatment of drug-resistant epilepsy. While epilepsy biomarkers detection for epileptogenic zone localization has traditionally relied on expert clinician knowledge, the use of deep learning in this context has recently gained appeal for objective diagnosis and reducing clinician burden. However, previously proposed classifiers for electrocorticogram data focused on only single biomarkers among many types, requiring separate models and large datasets for each. To minimize clinicians’ workload and patients’ sufferings, innovative methods to classify multiple biomarkers using small training datasets and minimal computation time is required. This research marks the first implementation of multi-modal multitask learning in epilepsy biomarker classification, proposing a model to automatically classify two biomarkers—high-frequency oscillations (HFO) and interictal epileptiform discharges (IED)—from separate datasets simultaneously. We validated the proposed model on 1500 annotated HFO and IED candidate signals each, obtained from 5 patients. Our proposed model can perform both HFO and IED classification simultaneously, with cross-validation accuracies of 90.99% and 93.99%;F1 scores were reported as 0.80 and 0.82, respectively. Our leave-one-patient-out experiments achieved 78.03% and 81.19% accuracy for HFO and IED classification, respectively. Compared to existing state-of-the-art classification architectures, our model is shown to be efficient and robust, yet simple and lightweight. The usefulness of sharing parameters between the two classifiers was confirmed in this practical clinical setting of epileptic biomarker detection, potentially contributing to minimizing the number of annotated datasets and realizing robust, accurate, and simultaneous identification of two biomarkers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Empathetic Response Generation System: Enhancing Photo Reminiscence Chatbot with Emotional Context Analysis

Empathetic Response Generation System: Enhancing Photo Remin...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Alberto Herrera Xiaobei Qian Li-Chen Fu Department of Computer Science and Information Engineering National Taiwan University Department of Electrical Engineering National Taiwan University Graduate Institute of Networking and Multimedia National Taiwan University

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Dementia affects 50 million people worldwide, underscoring the urgent need for effective interventions to enhance their well-being. While reminiscence intervention shows promise, its implementation is hindered by limited human resources, making machine-aided systems a viable automated solution for seamless photo-reminiscence sessions. In this paper, we introduce an empathetic response generation system specifically designed to enhance a question-only photo-reminiscence chatbot, with a focus on improving emotional context understanding and enhancing conversation engagement. We leverage Transformers to encode dialogue history, infer emotional states from user responses, and extract named entities. By combining template-based utterances with a retrieval chatbot, our system generates relevant and empathetic responses to user replies. Our system’s effectiveness is validated through human evaluations using a Likert-like scale to assess engagement levels. The results demonstrate that our approach surpasses both the question-only system and other models from existing works, including retrieval and generated models. This highlights our system’s potential to enhance interactions and engagement, advancing technology-driven interventions for dementia that improve well-being and quality of life.

关键词： Emotion recognition Natural languages Knowledge based systems Chatbots Transformers History Intelligent robots Dementia Testing

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