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Mayo Clinic Proceedings: Digital Health 2024年第4期2卷 632-644页

作者： Reis, Zilma Silveira Nogueira Pagano, Adriana Silvina Ramos de Oliveira, Isaias Jose Dias, Cristiane dos Santos Lage, Eura Martins Mineiro, Erico Franco Varella Pereira, Glaucia Miranda de Carvalho Gomes, Igor Basilio, Vinicius Araujo Cruz-Correia, Ricardo João de Jesus, Davi dos Reis de Souza Júnior, Antônio Pereira da Rocha, Leonardo Chaves Dutra Health Informatics Center Faculty of Medicine Universidade Federal de Minas Gerais Minas Gerais Belo Horizonte Brazil Arts Faculty Universidade Federal de Minas Gerais Minas Gerais Belo Horizonte Brazil Department of Pediatrics Faculty of Medicine Universidade Federal de Minas Gerais Minas Gerais Belo Horizonte Brazil Department of Design Technology School of Architecture Universidade Federal de Minas Gerais Minas Gerais Belo Horizonte Brazil Department of Obstetrics and Gynecology Faculty of Medical Sciences Universidade Estadual de Campinas São Paulo Campinas Brazil Departamento de Medicina da Comunidade Informação e Decisão em Saúde National Secretary of Primary Care of the Brazilian Ministry of Health Brasília Brazil MEDCIDS Porto University Porto Portugal Department of Computer Science Universidade Federal de São João Del Rei São João del Rei Minas Gerais Brazil

Objective: To assess the support of large language models (LLMs) in generating clearer and more personalized medication instructions to enhance e-prescription. Patients and Methods: We established patient-centered guidelines for adequate, acceptable, and personalized directions to enhance e-prescription. A dataset comprising 104 outpatient scenarios, with an array of medications, administration routes, and patient conditions, was developed following the Brazilian national e-prescribing standard. Three prompts were submitted to a closed-source LLM. The first prompt involved a generic command, the second one was calibrated for content enhancement and personalization, and the third one requested bias mitigation. The third prompt was submitted to an open-source LLM. Outputs were assessed using automated metrics and human evaluation. We conducted the study between March 1, 2024 and September 10, 2024. Results: Adequacy scores of our closed-source LLM's output showed the third prompt outperforming the first and second one. Full and partial acceptability was achieved in 94.3% of texts with the third prompt. Personalization was rated highly, especially with the second and third prompts. The 2 LLMs showed similar adequacy results. Lack of scientific evidence and factual errors were infrequent and unrelated to a particular prompt or LLM. The frequency of hallucinations was different for each LLM and concerned prescriptions issued upon symptom manifestation and medications requiring dosage adjustment or involving intermittent use. Gender bias was found in our closed-source LLM's output for the first and second prompts, with the third one being bias-free. The second LLM's output was bias-free. Conclusion: This study demonstrates the potential of LLM-supported generation to produce prescription directions and improve communication between health professionals and patients within the e-prescribing system. © 2024 The Authors

关键词： AI artificial intelligence EHR electronic health record ICPC-2 International Classification of Primary Care, 2nd edition LLM large language model e-SUS electronic Sistema Único de Saúde

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HealthyBlockchain for Global Patients

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computers, Materials & Continua 2021年第8期68卷 2431-2449页

作者： Shada A.Alsalamah Hessah A.Alsalamah Thamer Nouh Sara A.Alsalamah College of Computer and Information Sciences King Saud UniversityRiyadh11451Saudi Arabia Digital Health and Innovation Science DivisionWorld Health OrganizationGenevaCH-1211Switzerland MIT Department of Mechanical Engineering Massachusetts Institute of Technology(MIT)Cambridge02139MAUSA College of Engineering and Architecture Al Yamamah UniversityRiyadh11451Saudi Arabia Trauma and Acute Care Surgery Unit College of MedicineKing Saud UniversityRiyadh11451Saudi Arabia College of Computer and Information Sciences Al Imam Mohammad Ibn Saud Islamic UniversityRiyadh13318Saudi Arabia

An emerging healthcare delivery model is enabling a new era of clinical care based on well-informed decision-making *** healthcare information systems(HISs)fall short of adopting this model due to a conflict between information security needed to implement the new model and those already enforced locally to support traditional care ***,in recent times,the healthcare sector has shown a substantial interest in the potential of using blockchain technology for providing quality care to *** blockchain solution proposed so far has fully addressed emerging cross-organization information-sharing needs in *** this paper,we aim to study the use of blockchain in equipping struggling HISs to cope with the demands of the new healthcare delivery model,by proposing HealthyBlockchain as a granular patient-centered ledger that digitally tracks a patient’s medical transactions all along the treatment pathway to support the care *** patient-centered ledger is a neutral tamper-proof trail timestamp block sequence that governs distributed patient information across the decentralized discrete *** connects patients,clinicians,and healthcare providers to facilitate a transparent,trustworthy,and secure supporting platform.

关键词： Blockchain eHealth electronic health record global patient healthcare information system information security legacy system patient-centered care privacy smart contract trust

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Energy-aware load balancing of parallel evolutionary algorithms with heavy fitness functions in heterogeneous CPU-GPU architectures

Energy-aware load balancing of parallel evolutionary algorit...

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作者： Escobar, Juan José Ortega, Julio Díaz, Antonio Francisco González, Jesús Damas, Miguel Department of Computer Architecture and Technology CITIC University of Granada Spain

By means of the availability of mechanisms such as Dynamic Voltage and Frequency Scaling (DVFS) and heterogeneous architectures including processors with different power consumption profiles, it is possible to devise scheduling algorithms that are aware of both runtime and energy consumption in parallel programs. In this paper, we propose and evaluate a multi-objective (more specifically, a bi-objective) approach to distribute the workload among the processing cores in a given heterogeneous parallel CPU-GPU architecture. The aim of this distribution may be either to save energy without increasing the running time or to reach a trade-off among time and energy consumption. The parallel programs considered here are master-worker evolutionary algorithms where the evaluation of the fitness function for the individuals in the population demands the most part of the computing time. As many useful bioinformatics and data mining applications exhibit this kind of parallel profile, the proposed energy-aware approach for workload scheduling could be frequently applied. © 2018 John Wiley & Sons, Ltd.

关键词： Energy utilization

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EPRD: Exploiting prior knowledge for evidence-providing automatic rumor detection

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Neurocomputing 2024年 563卷

作者： Li, Jiawen Li, Ronghui Ni, Shiwen Kao, Hung-Yu Naval Architecture and Shipping College Guangdong Ocean University Guangdong Zhanjiang524000 China Technical Research Center for Ship Intelligence and Safety Engineering of Guangdong Province Guangdong Zhanjiang524000 China Intelligent Knowledge Management Lab National Cheng Kung University Tainan Taiwan Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

With the prevalence of social media platforms, rumors have been a serious social problem. Notably, existing rumor detection methods simply provide detection labels while ignoring their explanation. However, illustrating the reasons why a suspicious statement is a rumor is essential. To address this realistic scenario, we propose a novel Evidence-Providing Rumor Detection model called EPRD. EPRD incorporates a wide variety of information from both prior knowledge sources and current comments. It also learns bilaterally friendly representations for interpretable rumor detection. EPRD first retrieves evidence from prior knowledge sources and checks the relationship between the given statement and its evidence. Our model then constructs two heterogeneous graph objects to simulate the propagation layout of the current comments and evidence relationships. Finally, EPRD integrates the GraphSAGE component and attention mechanism to detect rumors. To the best of our knowledge, we propose the first model that incorporates prior knowledge to verify rumors and boost credibility. Experiments on two real-world Twitter datasets demonstrate that EPRD consistently exhibits the best rumor detection performance. Moreover, EPRD outperforms other baselines in the early rumor detection task. © 2023

关键词： Graph neural networks

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A cluster of FPAAs to recognize images using neural networks

TechRxiv

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TechRxiv 2021年

作者： Daniel, García Moreno Alberto, Del Barrio A. Guillermo, Botella Jennifer, Hasler The Department of Computer Architecture and Automation Complutense University of Madrid Madrid28040 Spain Georgia Institute of Technology GA Atlanta30332-0250 United States

Analog computing has been recovering its relevance in the recent years. FPAAs are the equivalent to FPGAs but in the analog domain. The main drawback of FPAAs is their reduced integration capacity. In order to increase the amount of analog resources, in this paper a cluster of 40 FPAAs is proposed. As a use case, a 19-8-6-4 feedforward Neural Network has been implemented on such cluster. With the help of a DCT-based software framework, this NN is able to classify 28x28 MNIST images. Results show that the analog network is able to obtain almost the same results as the software baseline network. I © 2021, CC BY-NC-SA.

关键词： computer programming

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Cacheap:Portable and Collaborative I/O Optimization for Graph Processing

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Journal of computer Science & technology 2019年第3期34卷 690-706页

作者： Peng Zhao Chen Ding Lei Liu Jiping Yu Wentao Han Xiao-Bing Feng State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China Department of Computer Science University of RochesterRochester 14623U.S.A. Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China

Increasingly there is a need to process graphs that are larger than the available memory on today's *** systems have been developed with grapli representations that are efficient and compact for out-of-core processing.A necessary task in these systems is memory *** paper presents a system called Cacheap which automatically and efficiently manages the available memory to maximize the speed of grapli processing,minimize the amount of disk access,and maximize the utilization of memory for graph *** has a simple interface that can be easily adopted by existing graph *** paper describes the new system,uses it in recent graph engines,and demonstrates its integer factor improvements in the speed of large-scale grapli processing.

关键词： out-of-core graph processing system I/O optimization memory cache graph analytics locality

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Non-Convex Potential Games for Finding Global Solutions to Sensor Network Localization

Non-Convex Potential Games for Finding Global Solutions to S...

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European Control Conference (ECC)

作者： Gehui Xu Guanpu Chen Yiguang Hong Baris Fidan Thomas Parisini Karl H. Johansson Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Control Science and Engineering Tongji University Shanghai China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai China Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo ON Canada Department of Electrical and Electronic Engineering Imperial College London London UK Department of Engineering and Architecture University of Trieste Trieste Italy

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Sensor network localization (SNL) problems require determining the physical coordinates of all sensors in a network. This process relies on the global coordinates of anchors and the available measurements between non-anchor and anchor nodes. Attributed to the intrinsic non-convexity, obtaining a globally optimal solution to SNL is challenging, as well as implementing corresponding algorithms. In this paper, we formulate a non-convex multi-player potential game for a generic SNL problem to investigate the identification condition of the global Nash equilibrium (NE) therein, where the global NE represents the global solution of SNL. We employ canonical duality theory to transform the non-convex game into a complementary dual problem. Then we develop a conjugation-based algorithm to compute the stationary points of the complementary dual problem. On this basis, we show an identification condition of the global NE: the stationary point of the proposed algorithm satisfies a duality relation. Finally, simulation results are provided to validate the effectiveness of the theoretical results.

关键词： Location awareness Coordinate measuring machines Simulation Current measurement Noise Europe Games

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Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models

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IEEE Transactions on Vehicular technology 2025年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Mingzhe Quek, Tony Q. S. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL United States Singapore University of Technology and Design Dept. of Information Systems Technology and Design 487372 Singapore

Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements. © 1967-2012 IEEE.

关键词： NP-hard

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A Key Factor in Traffic Management - Vehicle Speed Prediction Using Machine Learning

A Key Factor in Traffic Management - Vehicle Speed Predictio...

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International Conference on Computation, Automation and Knowledge Management (ICCAKM)

作者： Ayush Maurya Ashish Dwivedi Saurabh Pratap Suchi Dubey Fuli Zhou Vinod Kumar Shukla Department of Computer Science and Engineering Shambhunath Institute of Engineering and Technology Prayagraj U.P India Jindal Global Business School O.P. Jindal Global University Sonipat India Department of Mechanical Engineering Indian Institute of Technology (BHU) Varanasi India Manipal Academy of Higher Education Dubai United Arab Emirates College of Economics and Management Zhengzhou University of Light Industry Zhengzhou China Department of Engineering and Architecture Amity University Dubai United Arab Emirates

The analysis of transportation data is considered as an essential application for Machine Learning (ML) based-intelligent transportation design and control especially for safety and improving energy management. There is a requirement to identify efficient methods for optimizing transportation operations and enhance traffic management. This study adopts exploratory data analysis to analyse transportation data including year, month, vehicle type, time period, area type, functional classification. Predictive analytics is used to predict vehicle speed as dependent variable. The study adopts four supervised ML models such as Multiple Linear Regression (MLR), Random Forest Regression (RFR), Decision Tree (DT) and Artificial Neural Network (ANN) approach for analysing the speed prediction factors. Speed prediction assists as a foundation of advanced traffic management systems. The results from the findings suggest that ANN and RFR are the commendable model for performance analytics.

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Identification of Clinical Features Associated with Mortality in COVID-19 Patients

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Operations Research Forum 2023年第1期4卷 16页

作者： Eskandarian, Rahimeh Alizadehsani, Roohallah Behjati, Mohaddeseh Zahmatkesh, Mehrdad Sani, Zahra Alizadeh Haddadi, Azadeh Kakhi, Kourosh Roshanzamir, Mohamad Shoeibi, Afshin Hussain, Sadiq Khozeimeh, Fahime Darbandy, Mohammad Tayarani Joloudari, Javad Hassannataj Lashgari, Reza Khosravi, Abbas Nahavandi, Saeid Islam, Sheikh Mohammed Shariful Internal Medicine Research Center Semnan University of Medical Sciences Semnan Iran Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Victoria Australia Rajaei Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran Omid Hospital Iran University of Medical Sciences Tehran Iran Department of Biology Faculty of Basic Sciences Shahrekord Branch Islamic Azad University Shahrekord Iran Department of Computer Engineering Faculty of Engineering Fasa University Fasa 74617-81189 Iran Data Science and Computational Intelligence Institute University of Granada Granada Spain System Administrator Dibrugarh University Assam 786004 India School of Architecture Islamic Azad University Taft Taft *** Iran Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Department of Computer Engineering Amol Institute of Higher Education Amol Iran Institute of Medical Science and Technology Shahid Beheshti University Tehran Iran School of Engineering and Applied Sciences Harvard Paulson Harvard University Allston 02134 MA United States Institute for Physical Activity and Nutrition School of Exercise and Nutrition Sciences Deakin University Geelong 3220 VIC Australia Cardiovascular Division The George Institute for Global Health Newtown Australia Sydney Medical School University of Sydney Camperdown Australia

Understanding clinical features and risk factors associated with COVID-19 mortality is needed to early identify critically ill patients, initiate treatments and prevent mortality. A retrospective study on COVID-19 patients referred to a tertiary hospital in Iran between March and November 2020 was conducted. COVID-19-related mortality and its association with clinical features including headache, chest pain, symptoms on computerized tomography (CT), hospitalization, time to infection, history of neurological disorders, having a single or multiple risk factors, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia were investigated. Based on the investigation outcome, decision tree and dimension reduction algorithms were used to identify the aforementioned risk factors. Of the 3008 patients (mean age 59.3 ± 18.7 years, 44% women) with COVID-19, 373 died. There was a significant association between COVID-19 mortality and old age, headache, chest pain, low respiratory rate, oxygen saturation < 93%, need for a mechanical ventilator, having symptoms on CT, hospitalization, time to infection, neurological disorders, cardiovascular diseases and having a risk factor or multiple risk factors. In contrast, there was no significant association between mortality and gender, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia. Our results might help identify early symptoms related to COVID-19 and better manage patients according to the extracted decision tree. The proposed ML models identified a number of clinical features and risk factors associated with mortality in COVID-19 patients. These models if implemented in a clinical setting might help to early identify patients needing medical attention and care. However, more studies are needed to confirm these findings. © 2023, The Author(s).

关键词： COVID‐19 Machine learning Mortality Risk factors Symptoms

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