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Integrated medical resource consumption stratification in hospitalized patients:an Auto Triage Management model based on accurate risk,cost and length of stay prediction

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science China(Life sciences) 2022年第5期65卷 988-999页

作者： Qin Zhong Zongren Li Wenjun Wang Lei Zhang Kunlun He Medical Big Data Research Center Chinese PLA General HospitalBeijing 100853China Medical Artificial Intelligence Research Center Chinese PLA General HospitalBeijing 100853China Bio-engineering Research Center Chinese PLA General HospitalBeijing 100039China Department of Cardiology Fourth Medical Center of Chinese PLA General HospitalBeijing 100048China Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine Chinese PLA General HospitalBeijing 100048China China-Australia Joint Research Center for Infectious Diseases School of Public HealthXi’an Jiaotong University Health Science CenterXi’an 710061China

Triage management plays important roles in hospitalized patients for disease severity stratification and medical burden *** progression risks have been extensively researched for numbers of diseases,other crucial indicators that reflect patients’economic and time costs have not been systematically *** address the problems,we developed an automatic deep learning based Auto Triage Management(ATM)Framework capable of accurately modelling patients’disease progression risk and health economic *** on them,we can first discover the relationship between disease progression and medical system cost,find potential features that can more precisely aid patient triage in resource allocation,and allow treatment plan searching that has cured *** ATM in COVID-19,we built a joint model to predict patients’risk,the total length of stay(Lo S)and cost when at-admission,and remaining Lo S and cost at a given hospitalized time point,with C-index0.930 and 0.869 for risk prediction,mean absolute error(MAE)of 5.61 and 5.90 days for total Lo S prediction in internal and external validation data.

关键词： patient triage AutoML electronic medical records

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Environmental impacts assessment of ncm cathode material production of power lithium-ion batteries 20th

Environmental impacts assessment of ncm cathode material pro...

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20th Chinese Materials Conference, CMC 2019

作者： Jiao, Yulong Gao, Feng Xie, Jun College of Materials Science and Engineering Beijing University of Technology No.100 Ping Le Yuan Beijing100124 China National Engineering Laboratory for Industrial Big-data Application Technology China

ISBN: (纸本)9783035715668

The nickel cobalt manganese ternary (NCM) cathode material is one of the important parts of power lithium battery. The NCM cathode material production process including the Li2CO3 preparation, precursor preparation and synthesis process of cathode materials is resource-and energy-intensive, thus it leads to relatively severe environment pollution. In this article, life cycle assessment of NCM cathode material production using coprecipitation method in China was conducted based on ReCiPe method. The environmental impacts of the main processes were calculated. The main factors and sources causing environmental pressure were identified. The results indicated that the precursor production contributed the most to environmental impacts in the NCM cathode material production. The energy consumption of the precursor production was 612MJ, accounting for 94.6% of the total energy consumption of NCM production. The environmental impacts of the CoSO4 production and NiSO4 production were the main contributors to that of the precursor production. The primary cobalt and nickel resources in China with lower grade and difficult mining led to the high energy consumption of these two kinds of raw materials. Therefore, the development and utilization of the secondary cobalt and nickel metals will be a great potential to reduce environmental impacts of NCM production. © 2020 Trans Tech Publications Ltd, Switzerland.

关键词： Cobalt

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MFF-AMD: Multivariate Feature Fusion for Android Malware Detection 17th

MFF-AMD: Multivariate Feature Fusion for Android Malware Det...

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17th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2021

作者： Xu, Guangquan Feng, Meiqi Jiao, Litao Liu, Jian Dai, Hong-Ning Wang, Ding Panaousis, Emmanouil Zheng, Xi College of Intelligence and Computing Tianjin University Tianjin China Big Data School Qingdao Huanghai University Qingdao China Faculty of Information Technology Macau University of Science and Technology Macau SAR Taipa China School of EECS Peking University Beijing China University of Greenwich London United Kingdom Department of Computing Macquarie University Sydney Australia

ISBN: (纸本)9783030926342

Researchers have turned their focus on leveraging either dynamic or static features extracted from applications to train AI algorithms to identify malware precisely. However, the adversarial techniques have been continuously evolving and meanwhile, the code structure and application function have been designed in complex format. This makes Android malware detection more challenging than before. Most of the existing detection methods may not work well on recent malware samples. In this paper, we aim at enhancing the detection accuracy of Android malware through machine learning techniques via the design and development of our system called MFF-AMD. In our system, we first extract various features through static and dynamic analysis and obtain a multiscale comprehensive feature set. Then, to achieve high classification performance, we introduce the Relief algorithm to fuse the features, and design four weight distribution algorithms to fuse base classifiers. Finally, we set the threshold to guide MFF-AMD to perform static or hybrid analysis on the malware samples. Our experiments performed on more than 25,000 applications from the recent five-year dataset demonstrate that MFF-AMD can effectively detect malware with high accuracy. © 2021, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Android malware

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Research on the Trust Mechanism of Community Social Enterprises Based on Non-Linear Regression Model

Research on the Trust Mechanism of Community Social Enterpri...

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International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)

作者： He Tong Qing-Zhe Cui Wei Xu Ding-Ge Jin Meng-Ying Li Ting-Ting Xu Li-Xiang Xu Yuan-Yan Tang Department of Basic Chinese people's Liberation Army Aviation School Beijing China School of Artificial Intelligence and Big Data Hefei University Hefei China School of Public Afairs University of Science and Technology of China Hefei China Economics Teaching and Research Office Party School of Lu'an Municipal Committee of the Communist Party of China Zhuhai UM Science and Technology Research Institute FST University of Macau Macau

In recent years, social enterprises have gradually emerged as a major force in community governance. In order for social enterprises to better participate in community governance, this paper examines the trust mechanism between social enterprises and community governance, and offers suggestions for social enterprises in community governance. Firstly, this paper takes social trust as the object of study, divides it into ordinary trust and spe-cial trust, community governance effect as the dependent variable, selects intermediate variables to construct a non-linear regression model, and combines the correlation coefficient analysis to derive the mechanism of building trust relationship between social enterprises and community governance. Finally, there are sound suggestions for community governance aspects.

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Automatic Discovery of Multi-perspective Process Model using Reinforcement Learning

arXiv

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arXiv 2022年

作者： Sim, Sunghyun Liu, Ling Bae, Hyerim Industrial Management & Big Data Engineering Major Division of Industrial Convergence System Engineering Dongeui University 176 Eomgwang No Gaya Dong 24 Busanjin Gu Busan47340 Korea Republic of The College of Computing Georgia Institute of Technology 266 Ferst Drive AtlantaGA30332-0765 United States Industrial Data Science & Engineering Department of Industrial Engineering Pusan National University Pusan National University 30-Jan-jeon Dong Geum-Jeong Gu Busan609-753 Korea Republic of

Process mining is a methodology for derivation and analysis of processes models based on the event log. When process mining is employed to analyze business processes, the process discovery step, the conformance checking step, and the enhancements step are repeated. If a user wants to analyze a process from multiple perspectives (such as activity perspectives, originator perspectives, and time perspectives), the above procedure, inconveniently, has to be repeated over and over again. Although past studies involving process mining have applied detailed stepwise methodologies, no attempt has been made to incorporate and optimize multi-perspective process mining procedures. This paper contributes to developing solution approach to this problem. First, we propose an automatic discovery framework of a multi-perspective process model based on deep Q-Learning. Our Dual Experience Replay with Experience Distribution (DERED) approach can automatically perform process model discovery steps, conformance check steps, and enhancements steps. Second, we propose a new method that further optimizes the experience replay (ER) method, one of the key algorithms of deep Q-learning, to improve the learning performance of reinforcement learning agents. Finally, we validate our approach using six real world event datasets collected in port logistics, steel manufacturing, finance, IT, and government administration. We show that our DERED approach can provide users with multi-perspective, high-quality process models that can be employed more conveniently for multi-perspective process mining. © 2022, CC BY.

关键词： Reinforcement learning

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A Semi-supervised Bilingual Lexicon Induction Method for Distant Language Pairs Based on Bidirectional Adversarial Model

A Semi-supervised Bilingual Lexicon Induction Method for Dis...

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IEEE International Conference on big Knowledge (ICBK)

作者： Wenwu Zhi Yuhong Zhang Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9781665438599

Bilingual lexicon induction (BLI) can transfer knowledgefrom well- to under- resourced language, and has been widelyapplied to various NLP tasks. Recent work on BLI is projection-based that learns a mapping to connect source and target embedding spaces, with the isomorphism assumption. Unfortunately, the isomorphism assumption doesn't hold gener-ally, especially in typologically distant language pairs. Moreover, without supervised signals guiding, the training will further com-plicates BLI, making the performance of unsupervised methods unsatisfactory. To broke the restrict of isomorphism, we propose a semi-supervised method for distant BLI tasks, named A Semi-supervised Bilingual Lexicon Induction method in Latent Space based on Bidirectional Adversarial Model. First, two latent spaces are learned by two autoencoders for source and target domain independently to weaken the constraint of isomorphism in the embedding spaces. Then we add a few pairs of dictionary to learn the initial mapping to connect the Latent Space. Last, based on initial mapping, Cycle-Consistency is combined with Distance constraint constraint to maintain the geometry structure of both embedding spaces stable in the learning of bi-direction mapping based on adversarial model. By conducting extensive experiments, our method gets state-of-the-art results on most language pairs, especially with significant improvements on distant language pairs.

关键词： Training Geometry Dictionaries Conferences Bidirectional control Task analysis

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Learning Unified Embeddings for Recommendation via Meta-path Semantics 21

Learning Unified Embeddings for Recommendation via Meta-path...

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29th ACM International Conference on Multimedia, MM 2021

作者： Hao, Qianxiu Xu, Qianqian Yang, Zhiyong Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9781450386517

Heterogeneous information networks (HINs) have become a popular tool to capture complicated user-item relationships in recommendation problems in recent years. As a typical instantiation of HINs, meta-path is introduced in search of higher-level representations of user-item interactions. Though remarkable success has been achieved along this direction, existing meta-path-based recommendation methods face at least one of the following issues: 1) existing methods merely adopt simple meta-path fusion rules, which might be insufficient to exclude inconsistent information of different meta-paths that may hurt model performance;2) the representative power is limited by shallow/stage-wise formulations. To solve these issues, we propose an end-to-end and unified embedding-based recommendation framework with graph-based learning. To address 1), we propose a flexible fusion module to integrate meta-path-based similarities into relative similarities between users and items. To address 2), we take advantage of the powerful representative ability of deep neural networks to learn more complicated and flexible latent embeddings. Finally, empirical studies on real-world datasets demonstrate the effectiveness of our proposed method. © 2021 ACM.

关键词： Embeddings

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A reservation system with an efficient matching-and-scheduling mechanism for private parking-sharing programs 25

A reservation system with an efficient matching-and-scheduli...

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25th World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2021

作者： Yan, Pengyu Cai, Xiaoqiang Chu, Feng Ni, Debing School of Management and Economics University of Electronic Science and Technology of China Chengdu China Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology The Chinese University of Hong Kong Shenzhen Guangdong China Shenzhen Research Institute of Big Data Guangdong China Laboratoire IBISC Univ Evry Universitie Paris-Saclay Evry91025 France

ISBN: (纸本)9781713835196

We design a smart parking-sharing reservation system composed of three modules: Pre-processing, matching, and pricing. With the announced supply and demand information, the pre-processing module identifies a set of feasible parking slots for each driver concerning the compatibility of slot supply for the driver's demand. The matching module generates a system-wide optimal matching solution and further schedules the driver's parking time by using the flexibility of driver travel plans. The pricing module adopts a novel VCG-based pricing to determine the parking fees of drivers under an incomplete information setting in which each driver (player) cannot obtain the knowledge of the other competitors (players) who will compete against her. We find that the designed reservation system can not only elicit drivers' truthful demand reports but also achieve the optimal performance of the system. In this short paper we briefly describe our system and the main solution approaches we use. © WMSCI *** right reserved.

关键词： Costs

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Training time minimization for federated edge learning with optimized gradient quantization and bandwidth allocation

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Frontiers of Information technology & Electronic Engineering 2022年第8期23卷 1247-1263页

作者： Peixi LIU Jiamo JIANG Guangxu ZHU Lei CHENG Wei JIANG Wu LUO Ying DU Zhiqin WANG State Key Laboratory of Advanced Optical Communication Systems and Networks Department of ElectronicsPeking UniversityBeijing 100871China China Academy of Information and Communications Technology Beijing 100191China Shenzhen Research Institute of Big Data Shenzhen 518172China College of Information Science and Electronic Engineering Zhejiang UniversityHangzhou 310027China Zhejiang Provincial Key Laboratory of Information Processing Communication and NetworkingHangzhou 310027China

Training a machine learning model with federated edge learning(FEEL)is typically time consuming due to the constrained computation power of edge devices and the limited wireless resources in edge *** this study,the training time minimization problem is investigated in a quantized FEEL system,where heterogeneous edge devices send quantized gradients to the edge server via orthogonal *** particular,a stochastic quantization scheme is adopted for compression of uploaded gradients,which can reduce the burden of per-round communication but may come at the cost of increasing the number of communication *** training time is modeled by taking into account the communication time,computation time,and the number of communication *** on the proposed training time model,the intrinsic trade-off between the number of communication rounds and per-round latency is ***,we analyze the convergence behavior of the quantized FEEL in terms of the optimality ***,a joint data-and-model-driven fitting method is proposed to obtain the exact optimality gap,based on which the closed-form expressions for the number of communication rounds and the total training time are *** by the total bandwidth,the training time minimization problem is formulated as a joint quantization level and bandwidth allocation optimization *** this end,an algorithm based on alternating optimization is proposed,which alternatively solves the subproblem of quantization optimization through successive convex approximation and the subproblem of bandwidth allocation by bisection *** different learning tasks and models,the validation of our analysis and the near-optimal performance of the proposed optimization algorithm are demonstrated by the simulation results.

关键词： Federated edge learning Quantization optimization Bandwith allocation Training time minimization

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A Deep Learning-Based Paradigm for Retrieving Ocean Wave Spectra from Spaceborne SAR

A Deep Learning-Based Paradigm for Retrieving Ocean Wave Spe...

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SAR in big data Era: Models, Methods and Applications (bigSARdata)

作者： Ke Wu Xiao-Ming Li Chinese Acadamy of Sciences Key Laboratory of Digital Earth Science Aerospace Information Research Institute International Research Center of Big Data for Sustainable Development Goals University of Chinese Academy of Sciences Key Laboratory of Digital Earth of Hainan Province Hainan Aerospace Information Research Institute Hainan Aerospace Technology Innovation Center

The ocean wave spectrum is an essential parameter to fully describe the dynamic ocean waves. Although the spaceborne Synthetic Aperture Radar (SAR) is a potential instrument to measure the ocean wave spectrum, its retrieval is intricated by the complicated non-linear SAR-wave imaging mechanisms. This study introduced a deep learning (DL) method to address the ocean wave spectrum retrieval tasks from SAR images. The DL-based framework modifies the classical CNN architecture according to the multiple outputs of the target parameters and imposes a soft constraint based on the characteristics of the ocean wave spectrum. Trained and evaluated by the measurements of in-situ buoys deployed in the global ocean, the proposed model achieved root-mean-square errors of 0.53 m for significant wave heights and 1.24 s mean wave periods, demonstrating a generalized and robust performance. Our study represents a significant breakthrough in the longstanding challenge of SAR retrievals in ocean remote sensing.

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