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Incorporating Unannounced Meals and Exercise in Adaptive Learning of Personalized Models for Multivariable Artificial Pancreas Systems

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Journal of Diabetes Science and technology 2018年第5期12卷 953-966页

作者： Hajizadeh, Iman Rashid, Mudassir Turksoy, Kamuran Samadi, Sediqeh Feng, Jianyuan Sevil, Mert Hobbs, Nicole Lazaro, Caterina Maloney, Zacharie Littlejohn, Elizabeth Cinar, Ali Department of Chemical and Biological Engineering Illinois Institute of Technology Chicago IL United States Department of Biomedical Engineering Illinois Institute of Technology Chicago IL United States Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago IL United States Department of Pediatrics and Medicine Section of Endocrinology Kovler Diabetes Center University of Chicago Chicago IL United States

Background: Despite the recent advancements in the modeling of glycemic dynamics for type 1 diabetes mellitus, automatically considering unannounced meals and exercise without manual user inputs remains challenging. Method: An adaptive model identification technique that incorporates exercise information and estimates of the effects of unannounced meals obtained automatically without user input is proposed in this work. The effects of the unknown consumed carbohydrates are estimated using an individualized unscented Kalman filtering algorithm employing an augmented glucose-insulin dynamic model, and exercise information is acquired from noninvasive physiological measurements. The additional information on meals and exercise is incorporated with personalized estimates of plasma insulin concentration and glucose measurement data in an adaptive model identification algorithm. Results: The efficacy of the proposed personalized and adaptive modeling algorithm is demonstrated using clinical data involving closed-loop experiments of the artificial pancreas system, and the results demonstrate accurate glycemic modeling with the average root-mean-square error (mean absolute error) of 25.50 mg/dL (18.18 mg/dL) for six-step (30 minutes ahead) predictions. Conclusions: The approach presented is able to identify reliable time-varying individualized glucose-insulin models. © 2018 Diabetes technology Society.

关键词： artificial pancreas biomedical system linear systems modeling and identification recursive identification subspace methods

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A Perspective on Quantum Sensors from Basic Research to Commercial Applications

arXiv

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arXiv 2024年

作者： Oh, Eun Gregoire, Maxwell D. Black, Adam T. Hughes, K. Jeramy Kunz, Paul D. Larsen, Michael Lautier-Gaud, Jean Lee, Jongmin Schwindt, Peter D.D. Mouradian, Sara L. Narducci, Frank A. Sackett, Charles A. Laboratory for Physical Sciences College ParkMD20740 United States Air Force Research Laboratory AlbuquerqueNM87117 United States Naval Research Laboratory WashingtonDC20375 United States Fieldline Inc. BoulderCO80301 United States Army Research Laboratory AdelphiMD20783 United States Northrup Grumman Woodland HillsCA91367 United States Rue François Mitterrand Talence33400 France Sandia National Laboratories AlbuquerqueNM87123 United States AFRL/RVBYT Space Vehicles Directorate Section Head Quantum Optics Optical Sciences Division Fieldline Industries Inc. Quantum Science & Technology Branch Chief DEVCOM Army Research Laboratory Department of Physics The University of Texas at Austin AustinTX78712 United States Quantum Science Architect Mission Systems Sector Emerging Capabilities Development iXblue Quantum Sensors The Technical Staff Atom-Optic Sensing Department The Technical Staff Atom-Optic Sensing Department Electrical and Computer Engineering Department Department of Physics Physics Department University of Washington SeattleWA98105 United States Naval Postgraduate School MontereyCA93943 United States University of Virginia CharlottesvilleVA22904 United States

Quantum sensors represent a new generation of sensors with improved precision, accuracy, stability, and robustness to environmental effects compared to their classical predecessors. After decades of laboratory development, several types of quantum sensors are now commercially available or are part-way through the commercialization process. This article provides a brief description of the operation of a selection of quantum sensors that employ the principles of atom-light interactions and discusses progress toward packaging those sensors into products. This article covers quantum inertial and gravitational sensors, including gyroscopes, accelerometers, gravimeters, and gravity gradiometers that employ atom interferometry, nuclear magnetic resonance gyroscopes, atomic and spin-defect magnetometers, and Rydberg electric field sensors. © 2024, CC BY-NC-ND.

关键词： Gyroscopes

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Hidden figures underlying quality measures: revealing hidden racial inequalities in end-of-life cancer care delivery: a cohort study

The Lancet Regional Health - Americas

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The Lancet Regional Health - Americas 2025年 47卷

作者： Inas S. Khayal John J. Shin Gabriel A. Brooks Amber E. Barnato Ellesse-Roselee Akré Terri Lewinson Amro M. Farid A. James O'Malley The Dartmouth Institute for Health Policy and Clinical Practice Geisel School of Medicine at Dartmouth Lebanon NH 03756 USA Biomedical Data Science Geisel School of Medicine at Dartmouth Lebanon NH 03756 USA Department of Computer Science Dartmouth College Hanover NH 03755 USA Cancer Population Sciences Program Dartmouth Cancer Center Lebanon NH 03756 USA Department of Medicine Dartmouth Hitchcock Medical Center Lebanon NH 03756 USA Section of Palliative Care Department of Medicine Dartmouth Hitchcock Medical Center Lebanon NH 03756 USA Department of Health Policy and Management Johns Hopkins Bloomberg School of Public Health Baltimore MD 21205 USA Department of Systems Engineering School of Engineering and Science Stevens Institute of Technology Hoboken NJ 07030 USA Department of Mathematics Dartmouth College Hanover NH 03755 USA

Background Calls for healthcare systems to reduce disparities in cancer care access and outcomes draw on comparisons of existing measures across race and ethnicity subgroups. This approach may hide inequities driven by systematic bias in the timing of care delivery. The goals of this study were to: (1) identify differences in the timing of care delivery between racial groups, and (2) determine whether these differences could be identified from quality measures. Methods Retrospective decedent follow-back study of hospitals treating Medicare fee-for-service beneficiaries with advanced cancer aged 65–99 who died April–December 2016. Among hospitals serving at least 11 decedents of color (including Black or African-American, Asian/Pacific Islander, Hispanic, American Indian/Alaska Native, and Other) and 11 White decedents, we calculated hospital-level differences between White decedents and decedents of color for 1) any use of palliative care and hospice (Measures) and 2) daily counts of palliative care and hospice use for each day in the 6 months before death (Signatures). Findings The cohort included 30,319 decedents across 217 hospitals, of whom 7,852 (25.9%) were people of color (POC). The median of the hospital-specific aggregate measure difference was −5.35% (IQR = 12.83) for palliative care, indicating more POC received any palliative care, and 3.66% (IQR = 12.45) for hospice care, indicating more White people (WP) received any hospice care. We identified 5 high-level cluster-group descriptions of inequality from signatures. Inequality information from signatures matched those from measures in only 46.5% and 39.2% of hospitals for palliative and hospice care, respectively. Interpretation Signatures incorporating timing of care delivery using longitudinal data revealed patterns of racial-ethnic inequalities in end-of-life cancer care otherwise missed by traditional aggregate quality measures. Funding This work was supported by the American Cancer Society Award (RS

关键词： Racial/ethnic healthcare inequities Palliative care Hospice care Healthcare quality indicators

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Erratum: Expression of concern: "Integrating biochar in anaerobic digestion: Insights into diverse feedstocks and algal biochar" [Environ. Technol. Innov. 36 (2024) 103814] (Environmental technology & Innovation (2024) 36, (S2352186424002906), (10.1016/***.2024.103814))

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Environmental technology and Innovation 2025年

作者： Sirohi, Ranjna Kumar, Manish Vivekanand, V. Shakya, Amita Tarafdar, Ayon Singh, Rickwinder Sawarkar, Ankush D. Hoang, Anh Tuan Pandey, Ashok Sri Karan Narendra Agricultural University Jaipur Jobner303329 India Amity Institute of Environmental Sciences Amity University Uttar Pradesh Noida India Center for Energy & Environment Malaviya National Institute of Technology Rajasthan Jaipur302017 India Amity Institute of Biotechnology Amity University Chhattisgarh Raipur India Livestock Production and Management Section ICAR-Indian Veterinary Research Institute Uttar Pradesh Izatnagar243122 India Department of Computer Science and Engineering Visvesvaraya National Institute of Technology Maharashtra Nagpur440010 India Faculty of Automotive Engineering Dong A University Danang Viet Nam Centre for Energy and Environmental Sustainability Uttar Pradesh Lucknow226029 India Sustainability Cluster School of Engineering University of Petroleum and Energy Studies Uttarakhand Dehradun248007 India Kyung Hee University 26 Kyungheedae-ro Seoul Dongdaemun-gu02447 Korea Republic of Centre for Waste Management and Renewable Energy Saveetha Institute of Medical and Technical Sciences Chennai600077 India of Energy and Environment Korea University 145 Anam-ro Seoul Seongbuk-gu02841 Korea Republic of

This is a note of a temporary expression of concern related to the above-mentioned publication. The editors have been alerted to concerns regarding self-citations, incorrect references, misidentification of algae types, contradictory statements, species name errors, and mis-citations also reported in PubPeer - Integrating Biochar in Anaerobic Digestion: Insights into Di…. The concern and this note will remain appended to the above-mentioned article, pending the outcome of investigation by the Elsevier Research Integrity & Publishing Ethics team. © 2025 The Authors

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A feedforward artificial neural network model for classification and detection of type 2 diabetes

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Journal of Physics: Conference Series 2021年第1期1734卷

作者： Enoch A. Frimpong Ariyo Oluwasanmi Edward Y. Baagyere Qin Zhiguang School of Information and Software Engineering. University of Electronic Science and Technology of China Chengdu China No.4 Section 2 North Jianshe Road 610054 Chengdu P. R. China Department of Computer Science University for Development Studies Box TL 1350 Tamale Ghana

Efforts to enhance accuracy in medical diagnostics in molecular medicine have contributed to the wide use of artificial neural network (ANN) algorithms for disease detection due to its ability to process large medical datasets and integrate them into characterized outputs to avoid misdiagnosis. Typically, the application of ANNs have proven useful in sample analyses of patients with diabetes and in decision support systems. Over the years, various ANN models have been utilized in medical diagnostics; however, these approaches still maintain certain levels of error and have lesser training and testing accuracies in disease detection. In this study, we propose a Feedforward Artificial Neural Network (FFANN) model with a dense neural network architecture suitable for processing numeric and textual dataset. We carefully designed our model structure to have the ability to maximize the number of layers and nodes required to learn every feature of the dataset and also to perform effective computations but avoiding model under fitting and overfitting which occurs when less or more layers are used respectively. This approach puts our model ahead of other state-of-the-art prediction models which have been proposed in terms of performance as it achieved 97.27% and 96.09% training and testing accuracies, respectively, for type 2 diabetes detection on Pima Indian Diabetes dataset.

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Primate Markerless Pose Estimation and Movement Analysis Using DeepLabCut

Primate Markerless Pose Estimation and Movement Analysis Usi...

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International Conference on Informatics, Electronics & Vision (ICIEV)

作者： Rollyn Labuguen Dean Karlo Bardeloza Salvador Blanco Negrete Jumpei Matsumoto Kenichi Inoue Tomohiro Shibata Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology Kitakyushu Japan Department of Electronics Computer and Communications Engineering Ateneo de Manila University Quezon Philippines System Emotional Science University of Toyama Toyama Japan Systems Neuroscience Section Kyoto University

The analysis and understanding of primate behavior play a fundamental role in fields such as neuroscience, medicine, psychology, genetics, and more. This paper demonstrates an automatic detection of primate features by using an open-source deep learning toolset, DeepLabCut. We trained the deep neural network to locate 17 features and extract the monkey pose by relating the set of features detected. The model is trained with 5,967 manually annotated monkey images, which achieved train and test set errors of 3.61 and 19.72 unit pixels respectively. We also plotted the feature trajectories across multiple frames to show that the trained model can be used for analyzing behavior.

关键词： Feature extraction Training Predictive models Wrist Task analysis Nose Pose estimation

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Correction to: Global surface features contribute to human haptic roughness estimations

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Experimental brain research 2022年第4期240卷 1293页

作者： Huazhi Li Jiajia Yang Yinghua Yu Wu Wang Yulong Liu Mengni Zhou Qingqing Li Jingjing Yang Shiping Shao Satoshi Takahashi Yoshimichi Ejima Jinglong Wu Graduate School of Interdisciplinary Science and Engineering in Health Systems Okayama University 3-1-1 Tsushima-Naka Kita-ku Okayama 700-8530 Japan. Graduate School of Interdisciplinary Science and Engineering in Health Systems Okayama University 3-1-1 Tsushima-Naka Kita-ku Okayama 700-8530 Japan. yang@okayama-u.ac.jp. Section On Functional Imaging Methods National Institute of Mental Health Bethesda MD USA. yang@okayama-u.ac.jp. Section On Functional Imaging Methods National Institute of Mental Health Bethesda MD USA. School of Psychological and Cognitive Sciences Peking University Beijing China. Department of Teacher Education Wenzhou University Wenzhou China. School of Computer Science and Technology Changchun University of Science and Technology Changchun China. School of Social Welfare Yonsei University Seoul Korea. School of Mechatronical Engineering Beijing Institute of Technology Beijing China.

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Privacy preservation for ehealth big data in cloud accessed using resource-constrained devices: Survey

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International Journal of Network Security 2019年第2期21卷 312-325页

作者： Kibiwott, Kittur Philemon Fengli, Zhang Victor, K. Kimeli Anyembe, Omala A. Opoku-Mensah, Eugene School of Information and Software Engineering University of Electronic Science and Technology of China No.4 Section 2 Jianshe Rd North Chengdu610054 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China University of Eldoret P. O. Box 1125-30100 Eldoret Kenya

Mobile technology is proving to offer unprecedented advantage to health professionals by providing a more efficient transmission and access to health services. However, mobile devices are resource-constrained. This is setback whenever storage and computation are required on ehealth big data. To mitigate this drawback, mobile computing is integrated with scalable cloud computing. While this is an advantage on mobile user's side by enlarging the limited resources of the device, it also gives rise to security and privacy concerns. In order to overcome these challenges associated with security and privacy, the data owner (hospital) encrypts data using Attribute Based Encryption (ABE) primitive due to the fine-grained access control advantage it offers then sends ciphertext to the cloud. To realize fast data access, the resource-constrained device securely outsources heavy computations to resource abundant cloud server on its behalf with the guarantee that the server cannot learn anything about plaintext. In this paper, a survey of ABE with outsourced decryption of the existing works that is applicable to resource-constrained device for accessing eHealth big data is provided. © 2019 Femto Technique Co., Ltd.

关键词： Big data

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Solving Lur'e equations through zeroing neural networks

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Information Sciences 2025年 718卷

作者： Yu He Xiaojuan Wang Yafei Tie Huanqi Yang Theodore E. Simos Spyridon D. Mourtas Vasilios N. Katsikis School of Computer Science and Artificial Intelligence Huanghuai University Zhumadian 463000 China Henan International Joint Laboratory of Behavior Optimization Control for Smart Robots Henan 463000 China Henan Key Laboratory of Smart Lighting Zhumadian 46300 China Hangzhou Dianzi University School of Mechanical Engineering Er Hao Da Jie 1158 Xiasha Hangzhou 310018 China Center for Applied Mathematics and Bioinformatics Gulf University for Science and Technology West Mishref 32093 Kuwait Section of Mathematics Dept. of Civil Engineering Democritus Univ. of Thrace Xanthi 67100 Greece Department of Economics Division of Mathematics-Informatics and Statistics-Econometrics National and Kapodistrian University of Athens Sofokleous 1 Street 10559 Athens Greece Laboratory “Hybrid Methods of Modelling and Optimization in Complex Systems” Siberian Federal University Prosp. Svobodny 79 660041 Krasnoyarsk Russia

Solving Lur'e equations plays a critical role in addressing linear-quadratic optimal control (LQOC) problems, especially in cases where the control cost matrices are singular. This paper introduces, for the first time, two novel zeroing neural network (ZNN) models—ZNNLE and ZNNLE-LQOC—specifically designed to solve the Lur'e equation system and the LQOC problem, respectively. The proposed models extend the applicability of the ZNN methodology to these challenging scenarios by offering robust and efficient solutions to time-varying matrix equations. Theoretical analyses confirm the validity of both models, while numerical simulations and practical applications demonstrate their effectiveness. Moreover, a comparative study with an enhanced alternating-direction implicit (ADI) method highlights the superior performance of the ZNNLE-LQOC model in solving LQOC problems.

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Crowdsourcing airway annotations in chest computed tomography images

arXiv

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arXiv 2020年

作者： Cheplygina, Veronika Perez-Rovira, Adria Kuo, Wieying Tiddens, Harm A.W.M. de Bruijne, Marleen IMAG/e Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands Biomedical Imaging Group Rotterdam Department of Radiology and Nuclear Medicine Erasmus MC - University Medical Center Rotterdam Rotterdam Netherlands Department of Pediatric Pulmonology and Allergology Erasmus MC - Sophia Children’s Hospital Rotterdam Netherlands Department of Radiology and Nuclear Medicine Erasmus MC - University Medical Center Rotterdam Rotterdam Netherlands Machine Learning Section Department of Computer Science University of Copenhagen Denmark

Measuring airways in chest computed tomography (CT) scans is important for characterizing diseases such as cystic fibrosis, yet very time-consuming to perform manually. Machine learning algorithms offer an alternative, but need large sets of annotated scans for good performance. We investigate whether crowdsourcing can be used to gather airway annotations. We generate image slices at known locations of airways in 24 subjects and request the crowd workers to outline the airway lumen and airway wall. After combining multiple crowd workers, we compare the measurements to those made by the experts in the original scans. Similar to our preliminary study, a large portion of the annotations were excluded, possibly due to workers misunderstanding the instructions. After excluding such annotations, moderate to strong correlations with the expert can be observed, although these correlations are slightly lower than inter-expert correlations. Furthermore, the results across subjects in this study are quite variable. Although the crowd has potential in annotating airways, further development is needed for it to be robust enough for gathering annotations in practice. For reproducibility, data and code are available online: http://***/adriapr/***. © 2020, CC-BY.

关键词： Crowdsourcing

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