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Clinically Interpretable Machine Learning Models for Early Prediction of Mortality in Older Patients with Multiple Organ Dysfunction Syndrome: An International Multicenter Retrospective Study

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Journals of Gerontology - Series A Biological Sciences and Medical Sciences 2023年第4期78卷 718-726页

作者： Liu, Xiaoli DuMontier, Clark Hu, Pan Liu, Chao Yeung, Wesley Mao, Zhi Ho, Vanda Thoral, Patrick J. Kuo, Po-Chih Hu, Jie Li, Deyu Cao, Desen Mark, Roger G. Zhou, FeiHu Zhang, Zhengbo Celi, Leo Anthony Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education Beijing Advanced Innovation Center for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing China Laboratory for Computational Physiology Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA United States Center for Artificial Intelligence in Medicine General Hospital of PLA Beijing China New England Geriatric Research Education and Clinical Center VA Boston Healthcare System Boston MA United States Division of Aging Brigham and Women's Hospital Boston MA United States Department of Anesthesiology 920 Hospital of Joint Logistic Support Force of Chinese PLA Kunming Yunnan China Department of Critical Care Medicine First Medical Center General Hospital of PLA Beijing China Department of Medicine National University Hospital Singapore Division of Geriatric Medicine Department of Medicine National University Hospital Singapore Department of Intensive Care Medicine Amsterdam Netherlands Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Biomedical Engineering General Hospital of PLA Beijing China Elderly Center General Hospital of PLA Beijing China Department of Medicine Beth Israel Deaconess Medical Center Boston MA United States Department of Biostatistics Harvard T.H. Chan School of Public Health Boston MA United States

BACKGROUND: Multiple organ dysfunction syndrome (MODS) is associated with a high risk of mortality among older patients. Current severity scores are limited in their ability to assist clinicians with triage and management decisions. We aim to develop mortality prediction models for older patients with MODS admitted to the ICU. METHODS: The study analyzed older patients from 197 hospitals in the United States and 1 hospital in the Netherlands. The cohort was divided into the young-old (65-80 years) and old-old (≥80 years), which were separately used to develop and evaluate models including internal, external, and temporal validation. Demographic characteristics, comorbidities, vital signs, laboratory measurements, and treatments were used as predictors. We used the XGBoost algorithm to train models, and the SHapley Additive exPlanations (SHAP) method to interpret predictions. RESULTS: Thirty-four thousand four hundred and ninety-seven young-old (11.3% mortality) and 21 330 old-old (15.7% mortality) patients were analyzed. Discrimination AUROC of internal validation models in 9 046 U.S. patients was as follows: 0.87 and 0.82, respectively;discrimination of external validation models in 1 905 EUR patients was as follows: 0.86 and 0.85, respectively;and discrimination of temporal validation models in 8 690 U.S. patients: 0.85 and 0.78, respectively. These models outperformed standard clinical scores like Sequential Organ Failure Assessment and Acute Physiology Score III. The Glasgow Coma Scale, Charlson Comorbidity Index, and Code Status emerged as top predictors of mortality. CONCLUSIONS: Our models integrate data spanning physiologic and geriatric-relevant variables that outperform existing scores used in older adults with MODS, which represents a proof of concept of how machine learning can streamline data analysis for busy ICU clinicians to potentially optimize prognostication and decision making. © The Author(s) 2022. Published by Oxford University Press on behalf

关键词： International multicenter Interpretable models Machine learning Mortality Multiple organ dysfunction syndrome

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Efficient Routing Protection Algorithm Based on Optimized Network Topology

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computers, Materials & Continua 2022年第9期72卷 4525-4540页

作者： Haijun Geng Zikun Jin Jiangyuan Yao Han Zhang Zhiguo Hu Bo Yang Yingije Guo Wei Wang Qidong Zhang Guoao Duan School of Computer and Information Technology Shanxi UniversityShanxi030006China School of Automation and Software Engineering Shanxi UniversityShanxi030006China School of Computer Science and Technology Hainan UniversityHaikou570228China Department of Computer Science&Technology Tsinghua UniversityBeijing100084China Department of Urban&Regional Planning San JoséState University San JoséCA95192USA Key Laboratory of Embedded System and Service Computing(Tongji University) Ministry of EducationShanghai201804China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu610054China Beijinng University of Technology Beijing-Dublin International CollegeBeijing University of TechnologyBeijing100124China

Network failures are unavoidable and occur *** the network fails,intra-domain routing protocols deploying on the Internet need to undergo a long convergence *** this period,a large number of messages are discarded,which results in a decline in the user experience and severely affects the quality of service of Internet Service Providers(ISP).Therefore,improving the availability of intra-domain routing is a trending research question to be *** usually employs routing protection algorithms to improve intra-domain routing ***,existing routing protection schemes compute as many backup paths as possible to reduce message loss due to network failures,which increases the cost of the network and impedes the methods deployed in *** address the issues,this study proposes an efficient routing protection algorithm based on optimized network topology(ERPBONT).ERPBONT adopts the optimized network topology to calculate a backup path with the minimum path coincidence degree with the shortest path for all source ***,the backup path with the minimum path coincidence with the shortest path is described as an integer programming *** the simulated annealing algorithm ERPBONT is used to find the optimal ***,the algorithm is tested on the simulated topology and the real *** experimental results show that ERPBONT effectively reduces the path coincidence between the shortest path and the backup path,and significantly improves the routing availability.

关键词： Routing protection algorithm network availability network failure shortest path backup path

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Field Trial on DTMB-A for the Coverage Performance of HDTV Program Under High Mobility and Spectrum Efficiency

Field Trial on DTMB-A for the Coverage Performance of HDTV P...

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IEEE International Conference on Electrical engineering and Photonics (EExPolytech)

作者： Jian Song Changyong Pan Kewu Peng Hui Yang Wenxiu Guo Xiaotao Jiang Zongze Li Department of Electronic Engineering Beijing National Research Center for Information Science and Technology (BNRist) Tsinghua University Beijing China Shenzhen International Graduate School Tsinghua University Shenzhen China Peng Cheng Laboratory Shenzhen China Shenzhen City Key Laboratory of Digital TV System Shenzhen China

The coverage performance for digital Television/ Terrestrial Multimedia Broadcasting-Advance system and 3GPP “Further evolved Multimedia Broadcast Multicast Service”, long-term evolution-based broadcasting system defined by release 16 (R16) with different working parameters has been evaluated through the field trial in Shenzhen with a focus on high-speed mobility car with reasonably high spectrum efficiency. The trial uses the same transmitter facility of same the RF hardware, at the same frequency and with the same power. Results show that terrain is major deterministic factor for coverage performance for each system and well-designed longer time-interleaver for R16 is indispensable to users' satisfaction under this application scenario.

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Time-domain methods for quantifying dynamic cerebral blood flow autoregulation: Review and recommendations. A white paper from the Cerebrovascular Research Network (CARNet)

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Journal of Cerebral Blood Flow and Metabolism 2024年第9期44卷 1480-1514页

作者： Kostoglou, Kyriaki Bello-Robles, Felipe Brassard, Patrice Chacon, Max Claassen, Jurgen AHR Czosnyka, Marek Elting, Jan-Willem Hu, Kun Labrecque, Lawrence Liu, Jia Marmarelis, Vasilis Z Payne, Stephen J Shin, Dae Cheol Simpson, David Smirl, Jonathan Panerai, Ronney B Mitsis, Georgios D Department of Electrical and Computer Engineering McGill University Montreal QC Canada Institute of Neural Engineering Graz University of Technology Graz Austria Departamento de Ingeniería Informática Universidad de Santiago de Chile Santiago Chile Department of Kinesiology Faculty of Medicine Université Laval QC Canada Research Center of the Institut universitaire de cardiologie et de pneumologie de Québec Quebec QC Canada Department of Geriatrics Radboud University Medical Center Research Institute for Medical Innovation and Donders Institute Nijmegen Netherlands Cerebral Haemodynamics in Ageing and Stroke Medicine (CHiASM) Department of Cardiovascular Sciences University of Leicester Leicester United Kingdom Department of Clinical Neurosciences Neurosurgery Department University of Cambridge Cambridge United Kingdom Department of Neurology and Clinical Neurophysiology University Medical Center Groningen Groningen Netherlands Medical Biodynamics Program Division of Sleep and Circadian Disorders Brigham and Women's Hospital Harvard Medical School Boston MA United States Division of Sleep Medicine Harvard Medical School Boston MA United States Laboratory for Engineering and Scientific Computing Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Department Biomedical Engineering Viterbi School of Engineering University of Southern California Los Angeles CA United States Institute of Applied Mechanics National Taiwan University Taipei Taiwan Institute of Sound and Vibration Research University of Southampton Southampton United Kingdom Cerebrovascular Concussion Laboratory Faculty of Kinesiology University of Calgary Calgary AB Canada Sport Injury Prevention Research Centre Faculty of Kinesiology University of Calgary Calgary AB Canada Hotchkiss Brain Institute University of Calgary Calgary AB Canada NIHR Leicester Biomedical Research Centre Bri

Cerebral Autoregulation (CA) is an important physiological mechanism stabilizing cerebral blood flow (CBF) in response to changes in cerebral perfusion pressure (CPP). By maintaining an adequate, relatively constant supply of blood flow, CA plays a critical role in brain function. Quantifying CA under different physiological and pathological states is crucial for understanding its implications. This knowledge may serve as a foundation for informed clinical decision-making, particularly in cases where CA may become impaired. The quantification of CA functionality typically involves constructing models that capture the relationship between CPP (or arterial blood pressure) and experimental measures of CBF. Besides describing normal CA function, these models provide a means to detect possible deviations from the latter. In this context, a recent white paper from the Cerebrovascular Research Network focused on Transfer Function Analysis (TFA), which obtains frequency domain estimates of dynamic CA. In the present paper, we consider the use of time-domain techniques as an alternative approach. Due to their increased flexibility, time-domain methods enable the mitigation of measurement/physiological noise and the incorporation of nonlinearities and time variations in CA dynamics. Here, we provide practical recommendations and guidelines to support researchers and clinicians in effectively utilizing these techniques to study CA. © The Author(s) 2024.

关键词： CARNet cerebral autoregulation cerebral blood flow time-domain methods white paper

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Optimization on multi-user physical layer security of intelligent reflecting surface-aided VLC

arXiv

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arXiv 2022年

作者： Sun, Shiyuan Yang, Fang Song, Jian Han, Zhu Department Of Electronic Engineering Beijing National Research Center For Information Science And Technology Tsinghua University Beijing100084 China Key Laboratory Of Digital TV System Of Guangdong Province And Shenzhen City Research Institute Of Tsinghua University In Shenzhen Shenzhen518057 China Department Of Electrical And Computer Engineering The University Of Houston HoustonTX77004 United States Department Of Computer Science And Engineering Kyung Hee University Seoul446-701 Korea Republic of

This letter investigates physical layer security in intelligent reflecting surface (IRS)-aided visible light communication (VLC). Under the point source assumption, we first elaborate the system model in the scenario with multiple legitimate users and one eavesdropper, where the secrecy rate maximization problem is transformed into an assignment problem by objective function approximation. Then, an iterative Kuhn-Munkres algorithm is proposed to optimize the transformed problem, and its computational complexity is in the second-order form of the numbers of IRS units and transmitters. Moreover, numerical simulations are carried out to verify the approximation performance and the VLC secrecy rate improvement by IRS. Copyright © 2022, The Authors. All rights reserved.

关键词： Combinatorial optimization

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Multidimensional analysis of sensory perception and neurophysiological responses to marinated beef cooked by diverse methods

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Innovative Food Science and Emerging Technologies 2025年 103卷

作者： Yang, Tianyi Jiang, Wei Luo, Chencheng Hu, Jin Xu, Wei Zhang, Peng Jin, Xiaofei Zhou, Yuxuan Liu, Rui Qian, Zhiyu Yang, Yamin Department of Biomedical Engineering Key Laboratory of Multi-modal Brain-Computer Precision Drive Ministry of Industry and Information Technology Key Laboratory of Brain-Machine Intelligence Technology Ministry of Education Key Laboratory of Digital Medical Equipment and Technology of Jiangsu Province Nanjing University of Aeronautics and Astronautics Nanjing210016 China Program in Neuroscience & Behavioral Disorders Duke-NUS Medical School National University of Singapore Singapore169857 Singapore Department of Neurosurgery Huashan Hospital Shanghai Medical College Fudan University National Center for Neurological Disorders National Key Laboratory of Medical Neurobiology Institutes of Brain Science Shanghai Key Laboratory of Brain Function and Regeneration Institute of Neurosurgery Shanghai200040 China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing211166 China College of Food Science and Engineering Yangzhou University Yangzhou225127 China

The cooking process significantly influences consumer perception and preference for meat. This study systematically compares the sensory and neurophysiological responses elicited by beef processed via five cooking methods, including boiling (BO), sous-vide (SV), roasting (RO), pan-frying (PF), and deep-frying (DF). Sensory evaluations combining a check-all-that-apply (CATA) questionnaire and hedonic scaling revealed that RO, PF, and DF treatments produced richer flavors and more intense aromas, linked to higher hedonic ratings. In contrast, SV and BO yielded milder sensory profiles with lower flavor intensity and less consumer preference. Electroencephalography (EEG) analysis supported these findings by showing that high-heat methods elicited stronger neural responses, especially in the α, β, and γ frequency bands, which are associated with pleasure, appetite, and cognitive engagement, particularly prominent during olfactory and gustatory tests. Source localization further revealed greater activation in brain regions involved in sensory, reward, memory, emotion, and motivation for high-heat treated beef, demonstrating the connection between enhanced flavor complexity of high-temperature process and consumer appeal. Converging evidence from subjective sensory evaluation and neurophysiological analysis highlights the central role of thermal processing in enhancing food palatability and consumer satisfaction, offering practical insights for improving beef preparation in the food industry. © 2025 Elsevier Ltd

关键词： Neurons

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Candidate Evaluation with Multimodal Data-Driven for Recruitment 27th

Candidate Evaluation with Multimodal Data-Driven for Recru...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Wu, Xing Liu, Kehong Wang, Jianjia Yao, Junfeng Deng, Bin Lv, Rongqi Song, Jun School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China School of AI and Advanced Computing XJTLU Entrepreneur College Xi’an Jiaotong-Liverpool University Suzhou215412 China CSSC Seago System Technology Co. Ltd. Shanghai200010 China Science and Technology on Near-Surface Detection Laboratory Jangsu Wuxi214035 China Department of Geography Hong Kong Baptist University 999077 Hong Kong

ISBN: (纸本)9783031781858

In the field of intelligent recruitment, automated resume matching and non-contact interviews have significantly improved the efficiency of companies in finding suitable candidates. This corresponds to the techniques of person-job matching and AI interviews. However, current person-job matching methods lack substantial data support, while AI interview methods struggle to integrate deep information from multimodal data and provide comprehensive evaluations of candidates’ responses. To address these challenges, we propose a multimodal data-driven person-job evaluation model, comprising two key stages: a person-job matching method based on graph attention and a multimodal AI interview method. Using a dual-perspective graph neural network approach, we accomplish the screening of candidates and positions. In the second stage, we conduct a comprehensive evaluation of candidates’ interview performance based on text, audio, and image modalities, providing a more objective, consistent, and efficient interview assessment method. Experimental results demonstrate that our person-job matching method surpasses current popular techniques and effectively transfers features to the next stage. In our multimodal AI interview method, we achieve accurate scoring of candidate responses, assessment of intonation stress levels, and inference of their Big Five personality traits, comprehensively evaluating candidates from multiple perspectives. This confirms the superiority and efficiency of our approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Graph neural networks

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Sampling for Remote Estimation of an Ornstein-Uhlenbeck Process through Channel with Unknown Delay Statistics

arXiv

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arXiv 2023年

作者： Chen, Yuchao Tang, Haoyue Wang, Jintao Yang, Pengkun Tassiulas, Leandros the Department of Electronic Engineering Tsinghua University Beijing100084 China Key Laboratory of Digital TV System of Guangdong Province and Shenzhen City Research Institute of Tsinghua University in Shenzhen Shenzhen China Center for Statistical Science Tsinghua University Beijing100084 China Department of Electrical Engineering Institute for Network Science Yale University New HavenCT United States

In this paper, we consider sampling an Ornstein-Uhlenbeck (OU) process through a channel for remote estimation. The goal is to minimize the mean square error (MSE) at the estimator under a sampling frequency constraint when the channel delay statistics is unknown. Sampling for MSE minimization is reformulated into an optimal stopping problem. By revisiting the threshold structure of the optimal stopping policy when the delay statistics is known, we propose an online sampling algorithm to learn the optimum threshold using stochastic approximation algorithm and the virtual queue method. We prove that with probability 1, the MSE of the proposed online algorithm converges to the minimum MSE that is achieved when the channel delay statistics is known. The cumulative MSE gap of our proposed algorithm compared with the minimum MSE up to the (k + 1)-th sample grows with rate at most O(ln k). Our proposed online algorithm can satisfy the sampling frequency constraint theoretically. Finally, simulation results are provided to demonstrate the performance of the proposed algorithm. © 2023, CC BY.

关键词： Mean square error

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A Risk-Averse Just-In-Time Scheme for Learning-Based Operation of Microgrids With Coupled Electricity-Hydrogen-Ammonia Under Uncertainties

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IEEE Transactions on Sustainable Energy 2025年

作者： Li, Longyan Ning, Chao Pan, Guangsheng Zhang, Leiqi Gu, Wei Zhao, Liang Du, Wenli Shahidehpour, Mohammad Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China School of Electrical Engineering Southeast University Nanjing210096 China Zhejiang Key Laboratory of Distributed Generations and Microgrid Technology State Grid Zhejiang Electric Power Research Institute Hangzhou310014 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology China Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strategy linking hydrogen storage levels with the operation modes of ammonia synthesis is established to prevent cost-ineffective shutdowns. The proposed model substantially improves operational flexibility but results in a challenging nonlinear fractional program. Based upon this model, a data-driven RAJIT scheme is developed for the real-time rolling optimization of AHMGs. Unlike conventional one-size-fits-all schemes using one optimization method throughout, the data-driven RAJIT intelligently switches between cost-effective deterministic optimization and risk-averse online-learning distributionally robust optimization depending on actual risk profiles, thus capitalizing on the respective strengths of these two optimization methods. To facilitate the solution of the resulting nonlinear program, we develop an equivalent-reformulation-based solution methodology by leveraging a constraint-tightening technique. Numerical simulations demonstrate that the proposed scheme guarantees safety and yields an overall cost reduction up to 14.6% compared with several state-of-the-art methods. © 2025 IEEE.

关键词： Ammonia

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RR-CP: Reliable-Region-Based Conformal Prediction for Trustworthy Medical Image Classification

arXiv

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arXiv 2023年

作者： Zhang, Yizhe Wang, Shuo Zhang, Yejia Chen, Danny Z. School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing210094 China Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai200032 China Shanghai Key Laboratory of MICCAI Shanghai200032 China Department of Computer Science and Engineering University of Notre Dame Notre DameIN46556 United States

Conformal prediction (CP) generates a set of predictions for a given test sample such that the prediction set almost always contains the true label (e.g., 99.5% of the time). CP provides comprehensive predictions on possible labels of a given test sample, and the size of the set indicates how certain the predictions are (e.g., a set larger than one is ‘uncertain’). Such distinct properties of CP enable effective collaborations between human experts and medical AI models, allowing efficient intervention and quality check in clinical decision-making. In this paper, we propose a new method called Reliable-Region-Based Conformal Prediction (RR-CP), which aims to impose a stronger statistical guarantee so that an extremely low error rate (e.g., 0.5%) can be achieved in the test time, and under this constraint, the size of the prediction set is optimized to be small. We consider a small prediction set size an important measure only when the low error rate is achieved. Experiments on five public datasets show that our RR-CP performs well: with a reasonably small-sized prediction set, it achieves the user-specified low error rate significantly more frequently than exiting CP methods. © 2023, CC BY.

关键词： Forecasting

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