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Guidance of development,validation,and evaluation of algorithms for populating health status in observational studies of routinely collected data (DEVELOP-RCD)

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Military Medical research 2025年第5期 788-798页

作者： Wen Wang Ying-Hui Jin Mei Liu Qiao He Jia-Yue Xu Ming-Qi Wang Guo-Wei Li Bo Fu Si-Yu Yan Kang Zou Xin Sun Institute of Integrated Traditional Chinese and Western Medicine Chinese Evidence-Based Medicine and Cochrane China CenterWest China HospitalSichuan University NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan Sichuan Center of Technology Innovation for Real World Data Center for Evidence-Based and Translational Medicine Zhongnan Hospital of Wuhan University Department of Health Research Methods Evidence and ImpactMcMaster University Center for Clinical Epidemiology and Methodology Guangdong Second Provincial General Hospital Biostatistics Unit Research Institute at St.Joseph's Healthcare Hamilton School of Computer Science and Engineering University of Electronic Science and Technology of China West China School of Public Health and West China Fourth Hospital Sichuan University

Background:In recent years,there has been a growing trend in the utilization of observational studies that make use of routinely collected healthcare data (RCD).These studies rely on algorithms to identify specific health conditions (e.g.,diabetes or sepsis) for statistical ***,there has been substantial variation in the algorithm development and validation,leading to frequently suboptimal performance and posing a significant threat to the validity of study ***,these issues are often ***:We systematically developed guidance for the development,validation,and evaluation of algorithms designed to identify health status (DEVELOP-RCD).Our initial efforts involved conducting both a narrative review and a systematic review of published studies on the concepts and methodological issues related to algorithm development,validation,and ***,we conducted an empirical study on an algorithm for identifying *** on these findings,we formulated specific workflow and recommendations for algorithm development,validation,and evaluation within the ***,the guidance underwent independent review by a panel of 20 external experts who then convened a consensus meeting to finalize ***:A standardized workflow for algorithm development,validation,and evaluation was *** by specific health status considerations,the workflow comprises four integrated steps:assessing an existing algorithm’s suitability for the target health status;developing a new algorithm using recommended methods;validating the algorithm using prescribed performance measures;and evaluating the impact of the algorithm on study ***,13 good practice recommendations were formulated with detailed ***,a practical study on sepsis identification was included to demonstrate the application of this ***:The establishment of guidance is intended to aid researchers and clinician

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From 2d Images to 3d Model: Weakly Supervised Multi-View Face Reconstruction with Deep Fusion

SSRN

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SSRN 2024年

作者： Zhao, Weiguang Yang, Chaolong Ye, Jianan Zhang, Rui Yan, Yuyao Yang, Xi Dong, Bin Hussain, Amir Huang, Kaizhu Department of Computer Science University of Liverpool LiverpoolL69 7ZX United Kingdom Department of Electrical Engineering and Electronics University of Liverpool LiverpoolL69 7ZX United Kingdom Department of Foundational Mathematics Xi’an Jiaotong-Liverpool University Suzhou215123 China Department of Electrical and Electronic Engineering Xi’an Jiaotong-Liverpool University Suzhou215123 China School of Advanced Technology China Co. Ltd. Beijing100000 China School of Computing Edinburgh Napier University EdinburghEH11 4BN United Kingdom Data Science Research Center Duke Kunshan University Kunshan215316 China

While weakly supervised multi-view face reconstruction (MVR) is garnering increased attention, one critical issue still remains open: how to effectively fuse multiple image information to reconstruct high-precision 3D models. In this regard, we propose a novel model called Deep Fusion MVR (DF-MVR) to reconstruct high-precision 3D facial shapes from multi-view images. Specifically, we introduce MulEn-Unet, a multi-view encoding to single decoding framework with skip connections and attention. This design allows for the extraction, integration, and compensation of deep features with attention from multi-view images. Furthermore, we adopt the involution kernel to enrich deep fusion features with channel features. In addition, we develop the face parse network to learn, identify, and emphasize the critical common face area within multi-view images. Experiments on Pixel-Face and Bosphorus datasets indicate the superiority of our model. Without 3D annotation, DF-MVR achieves 5.2% and 3.0% RMSE improvement over the existing weakly supervised MVRs respectively on Pixel-Face and Bosphorus dataset. Code will be available publicly at https://***/weiguangzhao/DF_MVR. © 2024, The Authors. All rights reserved.

关键词： Image fusion

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Development of A Stochastic Traffic Environment with Generative Time-Series Models for Improving Generalization Capabilities of Autonomous Driving Agents 31

Development of A Stochastic Traffic Environment with Generat...

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31st IEEE Intelligent Vehicles Symposium, IV 2020

作者： Ozturk, Anil Gunel, Mustafa Burak Dal, Melih Yavas, Ugur Ure, Nazim Kemal Faculty of Computer Engineering Istanbul Technical University Turkey Faculty of Aeronautics and Astronautics Aerospace Engineering Istanbul Technical University Turkey Faculty of Computer Engineering Bogazici University Turkey Eatron Technologies Istanbul Turkey Itu Artificial Intelligence and Data Science Research Center Istanbul Technical University Department of Aeronautical Engineering Turkey

Automated lane changing is a critical feature for advanced autonomous driving systems. In recent years, reinforcement learning (RL) algorithms trained on traffic simulators yielded successful results in computing lane changing policies that strike a balance between safety, agility and compensating for traffic uncertainty. However, many RL algorithms exhibit simulator bias and policies trained on simple simulators do not generalize well to realistic traffic scenarios. In this work, we develop a data driven traffic simulator by training a generative adverserial network (GAN) on real life trajectory data. The simulator generates randomized trajectories that resembles real life traffic interactions between vehicles, which enables training the RL agent on much richer and realistic scenarios. We demonstrate through simulations that RL agents that are trained on GAN-based traffic simulator has stronger generalization capabilities compared to RL agents trained on simple rule-driven simulators. © 2020 IEEE.

关键词： Reinforcement learning

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Heliocentric Distance Dependence of Zodiacal Light Observed by Hayabusa2#

arXiv

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arXiv 2023年

作者： Tsumura, Kohji Matsuura, Shuji Sano, Kei Iwata, Takahiro Yano, Hajime Kitazato, Kohei Takimoto, Kohji Yamada, Manabu Morota, Tomokatsu Kouyama, Toru Hayakawa, Masahiko Yokota, Yasuhiro Tatsumi, Eri Matsuoka, Moe Sakatani, Naoya Honda, Rie Kameda, Shingo Suzuki, Hidehiko Cho, Yuichiro Yoshioka, Kazuo Ogawa, Kazunori Shirai, Kei Sawada, Hirotaka Sugita, Seiji Department of Natural Sciences Faculty of Science and Engineering Tokyo City University Tokyo158-8557 Japan Department of Physics School of Science and Technology Kwansei Gakuin University Hyogo669-1337 Japan Department of Space Systems Engineering School of Engineering Kyushu Institute of Technology Fukuoka804-8550 Japan Institute of Space and Astronautical Science Japan Aerospace Exploration Agency Kanagawa252-5210 Japan Department of Space and Astronautical Science The Graduate University of Advanced Studies Kanagawa240-0193 Japan Department of Computer Science and Engineering The University of Aizu Fukushima965-8580 Japan Planetary Exploration Research Center Chiba Institute of Technology Chiba275-0016 Japan Department of Earth and Planetary Science University of Tokyo Tokyo113-0033 Japan Digital Architecture Research Center National Institute of Advanced Industrial Science and Technology Tokyo135-0064 Japan Instituto de Astrofisica de Canarias University of La Laguna Tenerife Spain Geological Survey of Japan National Institute of Advanced Industrial Science and Technology Ibaraki305-8567 Japan Center for Data Science Ehime University Ehime790-8577 Japan Rikkyo University Tokyo171-8501 Japan Meiji University Kanagawa214-8571 Japan Department of Complexity Science and Engineering University of Tokyo Chiba277-8561 Japan JAXA Space Exploration Center Japan Aerospace Exploration Agency Kanagawa252-5210 Japan Kobe University Hyogo657-8501 Japan

Zodiacal light (ZL) is sunlight scattered by interplanetary dust particles (IDPs) at optical wavelengths. The spatial distribution of IDPs in the Solar System may hold an important key to understanding the evolution of the Solar System and material transportation within it. The number density of IDPs can be expressed as n(r) ∼ r−α, and the exponent α ∼ 1.3 was obtained by previous observations from interplanetary space by Helios 1/2 and Pioneer 10/11 in the 1970s and 1980s. However, no direct measurements of α based on ZL observations from interplanetary space outside Earth’s orbit have been performed since then. Here, we introduce initial results for the radial profile of the ZL at optical wavelengths observed over the range 0.76-1.06 au by ONC-T aboard the Hayabusa2# mission in 2021-2022. The ZL brightness we obtained is well reproduced by a model brightness, although there is a small excess of the observed ZL brightness over the model brightness at around 0.9 au. The radial power-law index we obtained is α = 1.30 ± 0.08, which is consistent with previous results based on ZL observations. The dominant source of uncertainty arises from the uncertainty in estimating the diffuse Galactic light (DGL). © 2023, CC BY.

关键词： Orbits

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Discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering 15th

Discovering Spatio-Temporal Patterns in Precision Agricultur...

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15th International Conference on Soft Computing Models in Industrial and Environmental applications, SOCO 2020

作者： Melgar-García, Laura Godinho, Maria Teresa Espada, Rita Gutiérrez-Avilés, David Brito, Isabel Sofia Martínez-Álvarez, Francisco Troncoso, Alicia Rubio-Escudero, Cristina Data Science & Big Data Lab Pablo de Olavide University Seville41013 Spain Department of Mathematical and Physical Sciences Polytechnic Institute of Beja Beja Portugal Center for Mathematics Fundamental Applications and Operations Research University of Lisboa Lisbon Portugal Associação dos Agricultores do Baixo Alentejo Beja Portugal Department of Engineering Polytechnic Institute of Beja Beja Portugal Instituto de Desenvolvimiento de Novas Tecnologias - Centre of Technology and Systems Lisbon Portugal Department of Computer Languages and Systems University of Seville Seville Spain

ISBN: (纸本)9783030578015

Agriculture has undergone some very important changes over the last few decades. The emergence and evolution of precision agriculture has allowed to move from the uniform site management to the site-specific management, with both economic and environmental advantages. However, to be implemented effectively, site-specific management requires within-field spatial variability to be well-known and characterized. In this paper, an algorithm that delineates within-field management zones in a maize plantation is introduced. The algorithm, based on triclustering, mines clusters from temporal remote sensing data. data from maize crops in Alentejo, Portugal, have been used to assess the suitability of applying triclustering to discover patterns over time, that may eventually help farmers to improve their harvests. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Precision agriculture

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Classification of Awareness in Disorders of Consciousness Using Common Spatial Pattern

Classification of Awareness in Disorders of Consciousness Us...

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International Winter Workshop on Brain-computer Interface (BCI)

作者： Chaewon Lee Sunho Lee Jitka Annen Ji-Hoon Jeong Marcello Massimini Silvia Casarotto Melanie Boly Olivier Bodart Aurore Thibaut Mario Rosanova Steven Laureys Olivia Gosseries Minji Lee Dept. Data Science The Catholic University of Korea Bucheon Republic of Korea Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon Republic of Korea Dept. GIGA-Research & Neurology University and University Hospital of Liege Liege Belgium Dept. Data Analysis University of Ghent Ghent Belgium Dept. Computer Science Chungbuk National University Cheongju Republic of Korea Dept. Biomedical and Clinical Sciences “L. Sacco” University of Milan Milan Italy Dept. Psychiatry Wisconsin Institute for Sleep and Consciousness University of Wisconsin Madison USA Coma Science Group GIGA-Consciousness GIGA Research Center University of Liège Liège Belgium Centre du Cerveau Neurology department University Hospital of Liège Liège Belgium

ISBN: (数字)9798331521929

ISBN: (纸本)9798331521936

Advances in intensive care have improved the survival rate of patients with severe acute brain injury, but diagnostic errors for patients with disorders of consciousness are still high. Accurate diagnosis of these patients is very important because effective treatment can vary depending on the diagnosis. In this study, we propose a framework for classifying unresponsive wakefulness syndrome and minimally conscious state, focusing on awareness. In particular, power spectral density and common spatial patterns were used together, considering that spatial information is a key feature in consciousness. The 16 patients with unresponsive wakefulness syndrome and 14 with minimally conscious state underwent resting-state electroencephalography measurements. In addition, we compared the performance by utilizing each frequency (delta, theta, alpha, beta, gamma bands) related to consciousness. As a result, the highest accuracy of 95.06% was achieved by the EEGNet classifier, especially in the beta frequency band. We demonstrated that spatial information is very important in consciousness, as we observed that classification performance improved when common spatial patterns were used. These results provide insight into various frameworks for diagnosing patients with disorders of consciousness and may help patients survive by increasing the diagnosis rate in the future.

关键词： Deep learning Accuracy Biological system modeling Pattern classification Focusing Signal processing Brain modeling Particle measurements Electroencephalography Robustness

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Efficient Federated Learning for aioT applications Using Knowledge Distillation

arXiv

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arXiv 2021年

作者： Liu, Tian Ling, Zhiwei Xia, Jun Fu, Xin Yu, Shui Chen, Mingsong MoE Engineering Research Center of Software/Hardware Co-design Technology and Application East China Normal University Shanghai200062 China Department of Information Science and Engineering Zaozhuang University Zaozhuang277160 China Department of Electrical and Computer Engineering University of Houston HoustonTX77204 United States School of Computer Science University of Technology Sydney Australia

As a promising distributed machine learning paradigm, Federated Learning (FL) trains a central model with decentralized data without compromising user privacy, which makes it widely used by Artificial Intelligence Internet of Things (aioT) applications. However, the traditional FL suffers from model inaccuracy, since it trains local models only using hard labels of data while useful information of incorrect predictions with small probabilities is ignored. Although various solutions try to tackle the bottleneck of the traditional FL, most of them introduce significant communication overhead, making the deployment of large-scale aioT devices a great challenge. To address the above problem, this paper presents a novel Distillation-based Federated Learning (DFL) method that enables efficient and accurate FL for aioT applications. By using Knowledge Distillation (KD), in each round of FL training, our approach uploads both the soft targets and local model gradients to the cloud server for aggregation, where the aggregation results are then dispatched to aioT devices for the next round of local training. During the DFL local training, in addition to hard labels, the model predictions approximate soft targets, which can improve model accuracy by leveraging the knowledge of soft targets. To further improve our DFL model performance, we design a dynamic adjustment strategy of loss function weights for tuning the ratio of KD and FL, which can maximize the synergy between soft targets and hard labels. Comprehensive experimental results on well-known benchmarks show that our approach can significantly improve the model accuracy of FL without introducing significant communication overhead. Copyright © 2021, The Authors. All rights reserved.

关键词： Distillation

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research on Sentiment Dictionary Based on Sentiment Analysis in News Domain 7

Research on Sentiment Dictionary Based on Sentiment Analysis...

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7th IEEE International Conference on Big data Security on Cloud, 7th IEEE International Conference on High Performance and Smart Computing, and 6th IEEE International Conference on Intelligent data and Security, BigdataSecurity/HPSC/IDS 2021

作者： Gao, Yihang Su, Peng Zhao, Hui Qiu, Meikang Liu, Meiqin Henan International Joint Laboratory of Theories Key Technologies on Intelligence Networks Henan University Kaifeng China Intelligent Data Processing Engineering Research Center of Henan Province Henan University Kaifeng China Educational Information Technology Laboratory Henan University Kaifeng China Texas Am University-Commerce Department of Computer Science CommerceTX75428 United States College of Electrical Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9781665439275

The known Chinese sentiment dictionary has universal applicability, lacks the expansion of proprietary domains, and lacks support for network words. This paper proposes a new approach and applies it to the Internet news sentiment analysis text provided by the CCF Big data Computing Intelligence Contest. First, the corpus is segmented in Chinese to obtain candidate sentiment words that have not appeared in the sentiment dictionary. The candidate sentiment words are calculated in different sentimental texts. The proportion of appearance is obtained, and the emotional tendency of candidate emotional words is obtained, and then it is merged with the emotional vocabulary ontology database to obtain the expanded emotional dictionary. Experiments show that the expanded sentiment dictionary is better than the known Chinese sentiment vocabulary ontology database in the above corpus. © 2021 IEEE.

关键词： Sentiment analysis

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Wireless Large ai Model: Shaping the ai-Native Future of 6G and Beyond

arXiv

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arXiv 2025年

作者： Zhu, Fenghao Wang, Xinquan Li, Xinyi Zhang, Maojun Chen, Yixuan Huang, Chongwen Yang, Zhaohui Chen, Xiaoming Zhang, Zhaoyang Jin, Richeng Huang, Yongming Feng, Wei Yang, Tingting Bai, Baoming Gao, Feifei Yang, Kun Liu, Yuanwen Muhaidat, Sami Yuen, Chau Huang, Kaibin Wong, Kai-Kit Niyato, Dusit Debbah, Mérouane College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China School of Automation Southeast University Nanjing210096 China Tsinghua University Beijing100084 China Pengcheng Laboratory Shenzhen518066 China State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China State Key Laboratory of Novel Software Technology Nanjing University Nanjing210008 China Suzhou215163 China The University of Hong Kong Hong Kong KU 6G Research Center Computer and Communication Engineering Khalifa University Abu Dhabi127788 United Arab Emirates School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates

The emergence of sixth-generation and beyond communication systems is expected to fundamentally transform digital experiences through introducing unparalleled levels of intelligence, efficiency, and connectivity. A promising technology poised to enable this revolutionary vision is the wireless large ai model (WLAM), characterized by its exceptional capabilities in data processing, inference, and decision-making. In light of these remarkable capabilities, this paper provides a comprehensive survey of WLAM, elucidating its fundamental principles, diverse applications, critical challenges, and future research opportunities. We begin by introducing the background of WLAM and analyzing the key synergies with wireless networks, emphasizing the mutual benefits. Subsequently, we explore the foundational characteristics of WLAM, delving into their unique relevance in wireless environments. Then, the role of WLAM in optimizing wireless communication systems across various use cases and the reciprocal benefits are systematically investigated. Furthermore, we discuss the integration of WLAM with emerging technologies, highlighting their potential to enable transformative capabilities and breakthroughs in wireless communication. Finally, we thoroughly examine the high-level challenges hindering the practical implementation of WLAM and discuss pivotal future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Sorting

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Development,validation,and transportability of several machine-learned,non-exercise-based VO_(2max)prediction models for older adults

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Journal of Sport and Health science 2024年第5期13卷 611-620页

作者： Benjamin T.Schumacher Michael J.LaMonte Andrea Z.LaCroix Eleanor M.Simonsick Steven P.Hooker Humberto Parada Jr. John Bellettiere Arun Kumar Herbert Wertheim School of Public Health and Human Longevity Science University of California San DiegoLa JollaCA 92093USA Department of Epidemiology and Environmental Health School of Public Health and Health ProfessionsUniversity at BuffaloState University of New YorkBuffaloNY 14214USA Translational Gerontology Branch Intramural Research ProgramNational Institute on AgingNational Institutes of HealthBaltimoreMD 21225USA College of Health and Human Services San Diego State UniversitySan DiegoCA 92182USA Division of Epidemiology and Biostatistics School of Public HealthSan Diego State UniversitySan DiegoCA 92182USA University of California San Diego Moores Cancer Center La JollaCA 92093USA Computer Science and Engineering and Halicioglu Data Science Institute University of California San DiegoLa JollaCA 92093USA

Background:There exist few maximal oxygen uptake(VO_(2max))non-exercise-based prediction equations,fewer using machine learning(ML),and none specifically for older *** direct measurement of VO_(2max)is infeasible in large epidemiologic cohort studies,we sought to develop,validate,compare,and assess the transportability of several ML VO_(2max)prediction ***:The Baltimore Longitudinal Study of Aging(BLSA)participants with valid VO2_(max)tests were included(n=1080).Least absolute shrinkage and selection operator,linear-and tree-boosted extreme gradient boosting,random forest,and support vector machine(SVM)algorithms were trained to predict VO_(2max)*** developed these algorithms for:(a)the overall BLSA,(b)by sex,(c)using all BLSA variables,and(d)variables common in aging ***,we quantified the associations between measured and predicted VO_(2max)and ***:The age was 69.0±10.4 years(mean±SD)and the measured VO_(2max)was 21.6±5.9 mL/kg/*** absolute shrinkage and selection operator,linear-and tree-boosted extreme gradient boosting,random forest,and support vector machine yielded root mean squared errors of 3.4 mL/kg/min,3.6 mL/kg/min,3.4 mL/kg/min,3.6 mL/kg/min,and 3.5 mL/kg/min,*** quartiles of measured VO_(2max)showed an inverse gradient in mortality *** VO_(2max)variables yielded similar effect estimates but were not robust to ***:Measured VO_(2max)is a strong predictor of *** ML can improve the accuracy of prediction as compared to simpler approaches but estimates of association with mortality remain sensitive to *** studies should seek to reproduce these results so that VO_(2max),an important vital sign,can be more broadly studied as a modifiable target for promoting functional resiliency and healthy aging.

关键词： Cardiorespiratory fitness Prediction algorithms Epidemiology Mortality

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