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Social Media-Based Surveillance Systems for health informatics Using Machine and Deep Learning Techniques:A Comprehensive Review and Open Challenges

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1167-1202页

作者： Samina Amin Muhammad Ali Zeb Hani Alshahrani Mohammed Hamdi Mohammad Alsulami Asadullah Shaikh Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information System College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic *** review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their ***,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by *** paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic *** has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation *** recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM *** paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM ***,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.

关键词： Social media epidemic machine learning deep learning health informatics pandemic

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Managing health Treatment by Optimizing Complex Lab-Developed Test Configurations: A health informatics Perspective

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Computers, Materials & Continua 2023年第6期75卷 6251-6267页

作者： Uzma Afzal Tariq Mahmood Ali Mustafa Qamar Ayaz H.Khan Department of Computer Science Federal Urdu University of Arts Science and TechnologyKarachiPakistan Department of Computer Science Institute of Business AdministrationKarachiPakistan Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Computer Engineering Department King Fahd University of Petroleum and MineralsDhahranSaudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence Dhahran31261Saudi Arabia

A complex Laboratory Developed Test(LDT)is a clinical test developed within a single *** is typically configured from many fea-ture constraints from clinical repositories,which are part of the existing Lab-oratory Information Management System(LIMS).Although these clinical repositories are automated,support for managing patient information with test results of an LDT is also integrated within the existing ***,the support to configure LDTs design needs to be made available even in standard LIMS *** manual configuration of LDTs is a complex process and can generate configuration inconsistencies because many constraints between features can remain *** is a risky process and can lead patients to undergo unnecessary *** proposed an optimized solution(opt-LDT)based on Genetic Algorithms to automate the configuration and resolve the inconsistencies in ***-LDT encodes LDT configuration as an optimization problem and generates a consistent configuration that satisfies the constraints of the *** tested and validated opt-LDT for a local secondary care hospital in a real healthcare *** results,averaged over ten runs,show that opt-LDT resolves 90%of inconsistencies while taking between 6 and 6.5 s for each ***,positive feedback based on a subjective questionnaire from clinicians regarding the performance,acceptability,and efficiency of opt-LDT motivates us to present our results for regulatory approval.

关键词： Artificial intelligence health informatics evolutionary algorithms genetic algorithms feature selection laboratory developed test

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Cascaded PFLANN Model for Intelligent health informatics in Detection of Respiratory Diseases from Speech Using Bio-inspired Computation

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Journal of Artificial Intelligence and Technology 2024年第2期4卷 124-131页

作者： Jagannath Dayal Pradhan L.V.Narasimha Prasad Tusar Kanti Dash Manisha Guduri Ganapati Panda Electronics and Communications Engineering C V Raman Global UniversityBhubaneswarIndia Department of Computer Science and Engineering Institute of Aeronautical EngineeringHyderabadIndia School of Computing and informatics University of LouisianaLafayetteLAUSA

Due to the recent developments in communications technology,cognitive computations have been used in smart healthcare techniques that can combine massive medical data,artificial intelligence,federated learning,bio-inspired computation,and the Internet of Medical *** has helped in knowledge sharing and scaling ability between patients,doctors,and clinics for effective treatment of ***-based respiratory disease detection and monitoring are crucial in this direction and have shown several promising *** the subject’s speech can be remotely recorded and submitted for further examination,it offers a quick,economical,dependable,and noninvasive prospective alternative detection ***,the two main requirements of this are higher accuracy and lower computational complexity and,in many cases,these two requirements do not correlate with each *** problem has been taken up in this paper to develop a low computational complexity-based neural network with higher accuracy.A cascaded perceptual functional link artificial neural network(PFLANN)is used to capture the nonlinearity in the data for better classification performance with low computational *** proposed model is being tested for multiple respiratory diseases,and the analysis of various performance matrices demonstrates the superior performance of the proposed model both in terms of accuracy and complexity.

关键词： AI-ML ASR FLANN health informatics neural network PFLANN

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Characteristics and Enlightenment of health informatics Education in American Universities

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Review of Educational Theory 2020年第4期3卷 49-53页

作者： Zhongyang Xu Zhiqian Meng Library of Hangzhou Medical College HangzhouZhejiang310000China Beijing Bairui Honglin Pest Control Technology Company Limited Beijing100000China

The demand for health information is increasing in China,and China has gradually paid attention to health informatics *** successful experience of American health informatics education can effectively promote the development of health informatics education in *** paper analyzes the main characteristics of health informatics education in American colleges and universities by literature survey and network survey,and concludes that Chinese colleges and universities should strengthen practical education,enhance teachers’strength,increase the form of educational projects,and perfect the curriculum content system.

关键词： USA University health informatics Education

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Functional Neuroimaging in the New Era of Big Data

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Genomics, Proteomics & Bioinformatics 2019年第4期17卷 393-401页

作者： Xiang Li Ning Guo Quanzheng Li Massachusetts General Hospital and Harvard Medical School

The field of functional neuroimaging has substantially advanced as a big data science in the past decade,thanks to international collaborative projects and community *** we conducted a literature review on functional neuroimaging,with focus on three general challenges in big data tasks:data collection and sharing,data infrastructure construction,and data analysis *** review covers a wide range of literature types including perspectives,database descriptions,methodology developments,and technical *** show how each of the challenges was proposed and addressed,and how these solutions formed the three core foundations for the functional neuroimaging as a big data science and helped to build the current data-rich and data-driven ***,based on our review of recent literature on the upcoming challenges and opportunities toward future scientific discoveries,we envisioned that the functional neuroimaging community needs to advance from the current foundations to better data integration infrastructure,methodology development toward improved learning capability,and multi-discipline translational research framework for this new era of big data.

关键词： Big data Neuroimaging Machine learning health informatics fMRI

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Topic prevalence and trends of metaverse in healthcare:a bibliometric analysis

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Data Science and Management 2024年第2期7卷 129-143页

作者： Pei Wu Donghua Chen Runtong Zhang Department of Management Science and Engineering School of Economics and ManagementTsinghua UniversityBeijing100083China Department of Artificial Intelligence School of Information Technology and ManagementUniversity of International Business and EconomicsBeijing100029China Department of Information Management Beijing Jiaotong UniversityBeijing100044China

Metaverse technology is an advanced form of virtual reality and augmented technologies. It merges the digital world with the real world, thus benefitting healthcare services. Medical informatics is promising in the metaverse. Despite the increasing adoption of the metaverse in commercial applications, a considerable research gap remains in the academic domain, which hinders the comprehensive delineation of research prospects for the metaverse in healthcare. This study employs text-mining methods to investigate the prevalence and trends of the metaverse in healthcare;in particular, more than 34,000 academic articles and news reports are analyzed. Subsequently, the topic prevalence, similarity, and correlation are measured using topic-modeling methods. Based on bibliometric analysis, this study proposes a theoretical framework from the perspectives of knowledge, socialization, digitization, and intelligence. This study provides insights into its application in healthcare via an extensive literature review. The key to promoting the metaverse in healthcare is to perform technological upgrades in computer science, telecommunications, healthcare services, and computational biology. Digitization, virtualization, and hyperconnectivity technologies are crucial in advancing healthcare systems. Realizing their full potential necessitates collective support and concerted effort toward the transformation of relevant service providers, the establishment of a digital economy value system, and the reshaping of social governance and health concepts. The results elucidate the current state of research and offer guidance for the advancement of the metaverse in healthcare.

关键词： Metaverse health informatics Text mining Bibliometric analysis

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Framework for Personalized Chronic Pain Management:Harnessing AI and Personality Insights for Effective Care

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Journal of Artificial Intelligence and Technology 2024年第2期4卷 132-144页

作者： Akshi Kumar Rahul Seewal Dipika Jain Ravleen Kaur Department of Computing Goldsmiths University of LondonLondonUK Chelsea and Westminster Hospital and East Kent Hospital NHS Find Trust UK MyPain■ UK Department of Computer Science&Engineering Delhi Technological UniversityNew DelhiIndia Department of Computer Science&Engineering Netaji Subhas University of TechnologyNew DelhiIndia

This paper introduces a cutting-edge framework for personalized chronic pain management,leveraging the power of artificial intelligence(AI)and personality *** explores the intricate relationship between personality traits and pain perception,expression,and management,identifying key correlations that influence an individual’s experience of *** integrating personality psychology with AI-driven personality assessment,this framework offers a novel approach to tailoring chronic pain management strategies for each patient’s unique personality *** highlights the relevance of well-established personality theories such as the Big Five and the Myers-Briggs Type Indicator(MBTI)in shaping personalized pain management ***,the paper introduces multimodal AI-driven personality assessment,emphasizing the ethical considerations and data collection processes necessary for its *** illustrative case studies,the paper exemplifies how this framework can lead to more effective and patient-centered pain relief,ultimately enhancing overall *** conclusion,the paper positions the need of an“AI-Powered Holistic Pain Management Initiative”which has the potential to transform chronic pain management by providing personalized,data-driven solutions and create a multifaceted research impact influencing clinical practice,patient outcomes,healthcare policy,and the broader scientific community’s understanding of personalized medicine and AI-driven interventions.

关键词： artificial intelligence health informatics pain personality traits

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The Impact of Information Technology on Admission Decisions

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Computer Technology and Application 2011年第7期2卷 539-544页

作者： Ofir Ben-Assuli Moshe Leshno Itamar Shabtai The Faculty of Business Administration Ono Academic College Kiryat Ono 55451 Israel The Faculty of Management Tel-Aviv University Tel-Aviv 69978 Israel The School of Economics College of Management Academic Studies Rishon LeZion 75490 Israel

Information systems have been adopted in many fields including the health industry. Indeed, keeping abreast of the advances of the technological age, many medical organizations have invested heavily in information technologies （IT）, aiming at improving medical decision-making and increasing its efficiency. Despite their obvious advantages, the systems do not always immediately provide the vital medical information required for critical decision-making, and the decisions that are based on this partial information may result in a decreased level of quality of care and unnecessary costs. The objective of this research is to evaluate the contribution of IT to decision-makers （physicians） at the point of care of emergency departments （EDs） by investigating whether the information systems （IS） have improved the medical outcomes, in the complex and highly stressful environment of the ED, with time constraints and overcrowding. The authors evaluated the contribution of the medical information to admission decisions by using the track log-file analysis method. The results were obtained using a unique database containing referrals to the ED from seven main hospitals in Israel. The authors＇ results lead to the major conclusion that viewing medical history contributes to admission decisions and clearly reduces the number of avoidable admissions.

关键词： Medical informatics medical decision-making health informatics

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On Monetizing Personal Wearable Devices Data:A Blockchain-based Marketplace for Data Crowdsourcing and Federated Machine Learning in healthcare

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Artificial Intelligence Advances 2022年第2期4卷 8-16页

作者： Mohamed Emish Hari Kishore Chaparala Zeyad Kelani Sean D.Young Department of informatics University of CaliforniaIrvine92697United States of America Department of Political Science Faculty of Economics and Political Science Cairo UniversityEgypt Department of Emergency Medicine University of CaliforniaIrvine92697United States of America

Machine learning advancements in healthcare have made data collected through smartphones and wearable devices a vital source of public health and medical *** wearable device data help to monitor,detect,and predict diseases and health conditions,some data owners hesitate to share such sensitive data with companies or researchers due to privacy ***,wearable devices have been recently available as commercial products;thus large,diverse,and representative datasets are not available to most *** this article,the authors propose an open marketplace where wearable device users securely monetize their wearable device records by sharing data with consumers(e.g.,researchers)to make wearable device data more available to healthcare *** secure the data transactions in a privacy-preserving manner,the authors use a decentralized approach using Blockchain and Non-Fungible Tokens(NFTs).To ensure data originality and integrity with secure validation,the marketplace uses Trusted Execution Environments(TEE)in wearable devices to verify the correctness of health *** marketplace also allows researchers to train models using Federated Learning with a TEE-backed secure aggregation of data users may not be willing to *** ensure user participation,we model incentive mechanisms for the Federated Learning-based and anonymized data-sharing approaches using *** authors also propose using payment channels and batching to reduce smart contact gas fees and optimize user *** widely adopted,it’s believed that TEE and Blockchain-based incentives will promote the ethical use of machine learning with validated wearable device data in healthcare and improve user participation due to incentives.

关键词： Wearable devices Data integrity Data validation Federated learning Blockchain Trusted execution environment health informatics healthcare data collection Data monetization

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Drug-Treatment Generation Combinatorial Algorithm Based on Machine Learning and Statistical Methodologies

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Open Journal of Applied Sciences 2023年第4期13卷 548-561页

作者： Karen Gishyan Institute for informatics and Automation Problems NAS RA Yerevan Armenia

Finding out the desired drug combinations is a challenging task because of the number of different combinations that exist and the adversarial effects that may arise. In this work, we generate drug combinations over multiple stages using distance calculation metrics from supervised learning, clustering, and a statistical similarity calculation metric for deriving the optimal treatment sequences. The combination generation happens for each patient based on the characteristics (features) observed during each stage of treatment. Our approach considers not the drug-to-drug (one-to-one) effect, but rather the effect of group of drugs with another group of drugs. We evaluate the combinations using an FNN model and identify future improvement directions.

关键词： Combinatorial Treatments health informatics Machine Learning

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