As a result of breakthroughs in computational approaches mixed with a boom in multi-omics data,the development of numerous digital medicines and bioinformatics tools have aided in speeding up the healthcare industry p...As a result of breakthroughs in computational approaches mixed with a boom in multi-omics data,the development of numerous digital medicines and bioinformatics tools have aided in speeding up the healthcare industry process.The traditional healthcare development method has been further rationalized with the introduction of artificial intelligence(AI),deep learning(DL),and machine learning(ML).Wide-ranging biological and clinical data in the form of big data,which is stored in various databases worldwide,serve as the raw material for AI-based methods and aid in the precise identification of patterns and models.These patterns and models can be used to identify novel therapeutically active molecules with significantly less time,financial investment,and workforce.This review article provides insights into understanding the principles of AI technologies such as next-generation sequencing(NGS),natural language processing(NLP),radiological images,patients-electronic medical records(EMR),and drug discovery as well as how they should be used in ethical,economic,and social ramifications of AI.This review also highlights various applications of AI in the healthcare industry,along with the analyses of different AI technologies.Additionally,it will offer helpful suggestions to assist decision-makers in creating an AI plan that would support their shift to a digital healthcare system.展开更多
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-ins...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 Things.It has helped in knowledge sharing and scaling ability between patients,doctors,and clinics for effective treatment of patients.Speech-based respiratory disease detection and monitoring are crucial in this direction and have shown several promising results.Since the subject’s speech can be remotely recorded and submitted for further examination,it offers a quick,economical,dependable,and noninvasive prospective alternative detection approach.However,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 other.This 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 complexity.The 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.展开更多
Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM...Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance.Since,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by SM.This 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 outbreaks.DL has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation results.In 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 analysis.This 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 analysis.Finally,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.展开更多
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 me...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.展开更多
Objective The study of medicine formulas is a core component of traditional Chinese medicine(TCM),yet traditional learning methods often lack interactivity and contextual understanding,making it challenging for beginn...Objective The study of medicine formulas is a core component of traditional Chinese medicine(TCM),yet traditional learning methods often lack interactivity and contextual understanding,making it challenging for beginners to grasp the intricate composition rules of formulas.To address this gap,we introduce Formula-S,a situated visualization method for TCM formula learning in augmented reality(AR)and evaluate its performance.This study aims to evaluate the effectiveness of Formula-S in enhancing TCM formula learning for beginners by comparing it with traditional text-based formula learning and web-based visualization.Methods Formula-S is an interactive AR tool designed for TCM formula learning,featuring three modes(3D,Web,and Table).The dataset included TCM formulas and herb properties extracted from authoritative references,including textbook and the SymMap database.In Formula-S,the hierarchical visualization of the formulas as herbal medicine compositions,is linked to the multidimensional herb attribute visualization and embedded in the real world,where real herb samples are presented.To evaluate its effectiveness,a controlled study(n=30)was conducted.Participants who had no formal TCM knowledge were tasked with herbal medicine identification,formula composition,and recognition.In the study,participants interacted with the AR tool through HoloLens 2.Data were collected on both task performance(accuracy and response time)and user experience,with a focus on task efficiency,accuracy,and user preference across the different learning modes.Results The situated visualization method of Formula-S had comparable accuracy to other methods but shorter response time for herbal formula learning tasks.Regarding user experience,our new approach demonstrated the highest system usability and lowest task load,effectively reducing cognitive load and allowing users to complete tasks with greater ease and efficiency.Participants reported that Formula-S enhanced their learning experience through its intuitive interface and immersive AR environment,suggesting this approach offers usability advantages for TCM education.Conclusions The situated visualization method in Formula-S offers more efficient and accurate searching capabilities compared to traditional and web-based methods.Additionally,it provides superior contextual understanding of TCM formulas,making it a promising new solution for TCM learning.展开更多
Purpose: Disseminating medical and health information is a mission of a public medical library. This paper describes a practice of a medical library in providing online access to health information for the general pub...Purpose: Disseminating medical and health information is a mission of a public medical library. This paper describes a practice of a medical library in providing online access to health information for the general public.Design/methodology/approach: A four-step workflow is developed to integrate and disseminate heterogeneous health information from medical associations. First, a raw data repository is developed to manage the original submissions from information providers.Second, each document in the raw data repository is represented in a standardized XML schema. Third, the medical terms are identified and manually annotated, enriching the semantics of health information. Lastly, all the semantically enriched XML documents are converted to HTMLs for online dissemination.Findings: A health information website, CHealth, was developed for Chinese speakers. It provides free online access for all without any login or IP constrains. CHealth is available at www.chealth.org.cn.Research limitations: The current workflow is time-consuming and labor-intensive due to the lack of information submission/exchange standard and commonly agreed-on consumer health terminology in Chinese.Originality/value: In this work, the target audience of the medical library has been extended from traditional academic/professional to the general public. Methodologies in library sciences have been combined with those in consumer health informatics in CHealth development.展开更多
1|DEVELOPMENT AND ADOPTION OF EHR IN THE UNITED STATES At present,health-care systems in the United States face enormous challenges in providing quality care,characterized by safe,effective,efficient,patientcentered,t...1|DEVELOPMENT AND ADOPTION OF EHR IN THE UNITED STATES At present,health-care systems in the United States face enormous challenges in providing quality care,characterized by safe,effective,efficient,patientcentered,timely,and equitable care while containing health-care costs[1,2].To understand and address patients'increasingly complicated health-care needs,we need safe access to quality information that is characterized by integrity,reliability,and accuracy[3],and establish mutually beneficial relationships among a multidisciplinary team of professionals[4].Traditional paper-based clinical workflow produces many issues such as illegible handwriting,inconvenient access,the possibility of computational prescribing errors,inadequate patient hand-offs,and drug administration errors.These problems can lead to medical errors,omissions,and duplications and,ultimately,poor patient outcomes and compromised quality of care[2].展开更多
A complex Laboratory Developed Test(LDT)is a clinical test developed within a single laboratory.It is typically configured from many fea-ture constraints from clinical repositories,which are part of the existing Lab-o...A complex Laboratory Developed Test(LDT)is a clinical test developed within a single laboratory.It 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 LIMS.Still,the support to configure LDTs design needs to be made available even in standard LIMS packages.The manual configuration of LDTs is a complex process and can generate configuration inconsistencies because many constraints between features can remain unsatisfied.It is a risky process and can lead patients to undergo unnecessary treatments.We proposed an optimized solution(opt-LDT)based on Genetic Algorithms to automate the configuration and resolve the inconsistencies in LDTs.Opt-LDT encodes LDT configuration as an optimization problem and generates a consistent configuration that satisfies the constraints of the features.We tested and validated opt-LDT for a local secondary care hospital in a real healthcare environment.Our results,averaged over ten runs,show that opt-LDT resolves 90%of inconsistencies while taking between 6 and 6.5 s for each configuration.Moreover,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.展开更多
The demand for health information is increasing in China,and China has gradually paid attention to health informatics education.The successful experience of American health informatics education can effectively promot...The demand for health information is increasing in China,and China has gradually paid attention to health informatics education.The successful experience of American health informatics education can effectively promote the development of health informatics education in China.This 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.展开更多
Machine learning advancements in healthcare have made data collected through smartphones and wearable devices a vital source of public health and medical insights.While wearable device data help to monitor,detect,and ...Machine learning advancements in healthcare have made data collected through smartphones and wearable devices a vital source of public health and medical insights.While 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 concerns.Moreover,wearable devices have been recently available as commercial products;thus large,diverse,and representative datasets are not available to most researchers.In 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 researchers.To 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 data.The marketplace also allows researchers to train models using Federated Learning with a TEE-backed secure aggregation of data users may not be willing to share.To ensure user participation,we model incentive mechanisms for the Federated Learning-based and anonymized data-sharing approaches using NFTs.The authors also propose using payment channels and batching to reduce smart contact gas fees and optimize user profits.If 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.展开更多
This paper introduces a cutting-edge framework for personalized chronic pain management,leveraging the power of artificial intelligence(AI)and personality insights.It explores the intricate relationship between person...This paper introduces a cutting-edge framework for personalized chronic pain management,leveraging the power of artificial intelligence(AI)and personality insights.It explores the intricate relationship between personality traits and pain perception,expression,and management,identifying key correlations that influence an individual’s experience of pain.By 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 profile.It 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 plans.Additionally,the paper introduces multimodal AI-driven personality assessment,emphasizing the ethical considerations and data collection processes necessary for its implementation.Through illustrative case studies,the paper exemplifies how this framework can lead to more effective and patient-centered pain relief,ultimately enhancing overall well-being.In 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.展开更多
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 tec...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.展开更多
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 m...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.展开更多
Objective: To explore possible consequences of short stays in hospitals, as these short contacts reduce the patients’ time for information and support. Method: A literature survey was carried out to get an insight in...Objective: To explore possible consequences of short stays in hospitals, as these short contacts reduce the patients’ time for information and support. Method: A literature survey was carried out to get an insight in possible consequences by summarizing the state of knowledge on how men with prostate cancer undergoing prostatec-tomy surgery experience their contacts with the healthcare professionals. Results: A consequence is that often men with prostate cancer, treated with prostatectomy surgery, do not receive the individualized support, infor-mation, and dialogue they need, which leads to feelings of uncertainty, insecurity, and loss of control. The men use the Internet in their search for information and support, which makes them able to stay in control and be active, responsible partners in their own course of treatment. Conclusion: For men to feel secure and certain the accessibility of the healthcare professionals and the healthcare professionals’ ability to individualize information and support are important aspects. Practice Implications: It is relevant to provide male cancer patients with tools that can underpin their contact to the healthcare professionals. Utilizing Web 2.0 technologies, Internet based tools can support exchange-ability, towards dialogue-based contacts, between men with prostate cancer and healthcare professionals.展开更多
New sequencing technologies such as Illumina/Solexa, SOLiD/ABI, and 454/Roche, revolutionized the biological researches. In this context, the SOLiD platform has a particular sequencing type, known as multiplex run, wh...New sequencing technologies such as Illumina/Solexa, SOLiD/ABI, and 454/Roche, revolutionized the biological researches. In this context, the SOLiD platform has a particular sequencing type, known as multiplex run, which enables the sequencing of several samples in a single run. It implies in cost reduction and simplifies the analysis of related samples. Meanwhile, this sequencing type requires an additional filtering step to ensure the reliability of the results. Thus, we propose in this paper a probabilistic model which considers the intrinsic characteristics of each sequencing to characterize multiplex runs and filter low-quality data, increasing the data analysis reliability of multiplex sequencing performed on SOLiD. The results show that the proposed model proves to be satisfactory due to: 1) identification of faults in the sequencing process;2) adaptation and development of new protocols for sample preparation;3) the assignment of a degree of confidence to the data generated;and 4) guiding a filtering process, without discarding useful sequences in an arbitrary manner.展开更多
The field of functional neuroimaging has substantially advanced as a big data science in the past decade,thanks to international collaborative projects and community efforts.Here we conducted a literature review on fu...The field of functional neuroimaging has substantially advanced as a big data science in the past decade,thanks to international collaborative projects and community efforts.Here 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 methods.The review covers a wide range of literature types including perspectives,database descriptions,methodology developments,and technical details.We 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 community.Furthermore,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.展开更多
Physiological computing uses human physiological data as system inputs in real time.It includes,or significantly overlaps with,brain-computer interfaces,affective computing,adaptive automation,health informatics,and p...Physiological computing uses human physiological data as system inputs in real time.It includes,or significantly overlaps with,brain-computer interfaces,affective computing,adaptive automation,health informatics,and physiological signal based biometrics.Physiological computing increases the communication bandwidth from the user to the computer,but is also subject to various types of adversarial attacks,in which the attacker deliberately manipulates the training and/or test examples to hijack the machine learning algorithm output,leading to possible user confusion,frustration,injury,or even death.However,the vulnerability of physiological computing systems has not been paid enough attention to,and there does not exist a comprehensive review on adversarial attacks to them.This study fills this gap,by providing a systematic review on the main research areas of physiological computing,different types of adversarial attacks and their applications to physiological computing,and the corresponding defense strategies.We hope this review will attract more research interests on the vulnerability of physiological computing systems,and more importantly,defense strategies to make them more secure.展开更多
文摘As a result of breakthroughs in computational approaches mixed with a boom in multi-omics data,the development of numerous digital medicines and bioinformatics tools have aided in speeding up the healthcare industry process.The traditional healthcare development method has been further rationalized with the introduction of artificial intelligence(AI),deep learning(DL),and machine learning(ML).Wide-ranging biological and clinical data in the form of big data,which is stored in various databases worldwide,serve as the raw material for AI-based methods and aid in the precise identification of patterns and models.These patterns and models can be used to identify novel therapeutically active molecules with significantly less time,financial investment,and workforce.This review article provides insights into understanding the principles of AI technologies such as next-generation sequencing(NGS),natural language processing(NLP),radiological images,patients-electronic medical records(EMR),and drug discovery as well as how they should be used in ethical,economic,and social ramifications of AI.This review also highlights various applications of AI in the healthcare industry,along with the analyses of different AI technologies.Additionally,it will offer helpful suggestions to assist decision-makers in creating an AI plan that would support their shift to a digital healthcare system.
文摘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 Things.It has helped in knowledge sharing and scaling ability between patients,doctors,and clinics for effective treatment of patients.Speech-based respiratory disease detection and monitoring are crucial in this direction and have shown several promising results.Since the subject’s speech can be remotely recorded and submitted for further examination,it offers a quick,economical,dependable,and noninvasive prospective alternative detection approach.However,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 other.This 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 complexity.The 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.
基金authors are thankful to the Deanship of Scientific Research at Najran University for funding this work,under the Research Groups Funding Program Grant Code(NU/RG/SERC/12/27).
文摘Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance.Since,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by SM.This 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 outbreaks.DL has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation results.In 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 analysis.This 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 analysis.Finally,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.
基金supported by the National Natural Science Foundation of China(Grant No.:62102087)Fundamental Research Funds for the Central Universities in UIBE(Grant No.:22PY055-62102087)Scientific Research Laboratory of AI Technology and Applications,UIBE.
文摘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.
文摘Objective The study of medicine formulas is a core component of traditional Chinese medicine(TCM),yet traditional learning methods often lack interactivity and contextual understanding,making it challenging for beginners to grasp the intricate composition rules of formulas.To address this gap,we introduce Formula-S,a situated visualization method for TCM formula learning in augmented reality(AR)and evaluate its performance.This study aims to evaluate the effectiveness of Formula-S in enhancing TCM formula learning for beginners by comparing it with traditional text-based formula learning and web-based visualization.Methods Formula-S is an interactive AR tool designed for TCM formula learning,featuring three modes(3D,Web,and Table).The dataset included TCM formulas and herb properties extracted from authoritative references,including textbook and the SymMap database.In Formula-S,the hierarchical visualization of the formulas as herbal medicine compositions,is linked to the multidimensional herb attribute visualization and embedded in the real world,where real herb samples are presented.To evaluate its effectiveness,a controlled study(n=30)was conducted.Participants who had no formal TCM knowledge were tasked with herbal medicine identification,formula composition,and recognition.In the study,participants interacted with the AR tool through HoloLens 2.Data were collected on both task performance(accuracy and response time)and user experience,with a focus on task efficiency,accuracy,and user preference across the different learning modes.Results The situated visualization method of Formula-S had comparable accuracy to other methods but shorter response time for herbal formula learning tasks.Regarding user experience,our new approach demonstrated the highest system usability and lowest task load,effectively reducing cognitive load and allowing users to complete tasks with greater ease and efficiency.Participants reported that Formula-S enhanced their learning experience through its intuitive interface and immersive AR environment,suggesting this approach offers usability advantages for TCM education.Conclusions The situated visualization method in Formula-S offers more efficient and accurate searching capabilities compared to traditional and web-based methods.Additionally,it provides superior contextual understanding of TCM formulas,making it a promising new solution for TCM learning.
基金supported by the National Key Technology R&D Program of China (Grant No.:2013BAI06B01)
文摘Purpose: Disseminating medical and health information is a mission of a public medical library. This paper describes a practice of a medical library in providing online access to health information for the general public.Design/methodology/approach: A four-step workflow is developed to integrate and disseminate heterogeneous health information from medical associations. First, a raw data repository is developed to manage the original submissions from information providers.Second, each document in the raw data repository is represented in a standardized XML schema. Third, the medical terms are identified and manually annotated, enriching the semantics of health information. Lastly, all the semantically enriched XML documents are converted to HTMLs for online dissemination.Findings: A health information website, CHealth, was developed for Chinese speakers. It provides free online access for all without any login or IP constrains. CHealth is available at www.chealth.org.cn.Research limitations: The current workflow is time-consuming and labor-intensive due to the lack of information submission/exchange standard and commonly agreed-on consumer health terminology in Chinese.Originality/value: In this work, the target audience of the medical library has been extended from traditional academic/professional to the general public. Methodologies in library sciences have been combined with those in consumer health informatics in CHealth development.
基金funded by the Organized Research and Creative Activities(ORCA)Program at the University of Houston-Downtown(PI:Song Ge)。
文摘1|DEVELOPMENT AND ADOPTION OF EHR IN THE UNITED STATES At present,health-care systems in the United States face enormous challenges in providing quality care,characterized by safe,effective,efficient,patientcentered,timely,and equitable care while containing health-care costs[1,2].To understand and address patients'increasingly complicated health-care needs,we need safe access to quality information that is characterized by integrity,reliability,and accuracy[3],and establish mutually beneficial relationships among a multidisciplinary team of professionals[4].Traditional paper-based clinical workflow produces many issues such as illegible handwriting,inconvenient access,the possibility of computational prescribing errors,inadequate patient hand-offs,and drug administration errors.These problems can lead to medical errors,omissions,and duplications and,ultimately,poor patient outcomes and compromised quality of care[2].
文摘A complex Laboratory Developed Test(LDT)is a clinical test developed within a single laboratory.It 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 LIMS.Still,the support to configure LDTs design needs to be made available even in standard LIMS packages.The manual configuration of LDTs is a complex process and can generate configuration inconsistencies because many constraints between features can remain unsatisfied.It is a risky process and can lead patients to undergo unnecessary treatments.We proposed an optimized solution(opt-LDT)based on Genetic Algorithms to automate the configuration and resolve the inconsistencies in LDTs.Opt-LDT encodes LDT configuration as an optimization problem and generates a consistent configuration that satisfies the constraints of the features.We tested and validated opt-LDT for a local secondary care hospital in a real healthcare environment.Our results,averaged over ten runs,show that opt-LDT resolves 90%of inconsistencies while taking between 6 and 6.5 s for each configuration.Moreover,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.
文摘The demand for health information is increasing in China,and China has gradually paid attention to health informatics education.The successful experience of American health informatics education can effectively promote the development of health informatics education in China.This 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.
文摘Machine learning advancements in healthcare have made data collected through smartphones and wearable devices a vital source of public health and medical insights.While 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 concerns.Moreover,wearable devices have been recently available as commercial products;thus large,diverse,and representative datasets are not available to most researchers.In 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 researchers.To 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 data.The marketplace also allows researchers to train models using Federated Learning with a TEE-backed secure aggregation of data users may not be willing to share.To ensure user participation,we model incentive mechanisms for the Federated Learning-based and anonymized data-sharing approaches using NFTs.The authors also propose using payment channels and batching to reduce smart contact gas fees and optimize user profits.If 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.
文摘This paper introduces a cutting-edge framework for personalized chronic pain management,leveraging the power of artificial intelligence(AI)and personality insights.It explores the intricate relationship between personality traits and pain perception,expression,and management,identifying key correlations that influence an individual’s experience of pain.By 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 profile.It 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 plans.Additionally,the paper introduces multimodal AI-driven personality assessment,emphasizing the ethical considerations and data collection processes necessary for its implementation.Through illustrative case studies,the paper exemplifies how this framework can lead to more effective and patient-centered pain relief,ultimately enhancing overall well-being.In 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.
文摘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.
文摘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.
基金The Novo Nordisk Foundation DOF Det Obelske Familiefond+1 种基金 Danish Nursing Research Society Harboefonden
文摘Objective: To explore possible consequences of short stays in hospitals, as these short contacts reduce the patients’ time for information and support. Method: A literature survey was carried out to get an insight in possible consequences by summarizing the state of knowledge on how men with prostate cancer undergoing prostatec-tomy surgery experience their contacts with the healthcare professionals. Results: A consequence is that often men with prostate cancer, treated with prostatectomy surgery, do not receive the individualized support, infor-mation, and dialogue they need, which leads to feelings of uncertainty, insecurity, and loss of control. The men use the Internet in their search for information and support, which makes them able to stay in control and be active, responsible partners in their own course of treatment. Conclusion: For men to feel secure and certain the accessibility of the healthcare professionals and the healthcare professionals’ ability to individualize information and support are important aspects. Practice Implications: It is relevant to provide male cancer patients with tools that can underpin their contact to the healthcare professionals. Utilizing Web 2.0 technologies, Internet based tools can support exchange-ability, towards dialogue-based contacts, between men with prostate cancer and healthcare professionals.
文摘New sequencing technologies such as Illumina/Solexa, SOLiD/ABI, and 454/Roche, revolutionized the biological researches. In this context, the SOLiD platform has a particular sequencing type, known as multiplex run, which enables the sequencing of several samples in a single run. It implies in cost reduction and simplifies the analysis of related samples. Meanwhile, this sequencing type requires an additional filtering step to ensure the reliability of the results. Thus, we propose in this paper a probabilistic model which considers the intrinsic characteristics of each sequencing to characterize multiplex runs and filter low-quality data, increasing the data analysis reliability of multiplex sequencing performed on SOLiD. The results show that the proposed model proves to be satisfactory due to: 1) identification of faults in the sequencing process;2) adaptation and development of new protocols for sample preparation;3) the assignment of a degree of confidence to the data generated;and 4) guiding a filtering process, without discarding useful sequences in an arbitrary manner.
基金supported by the National Institutes of Health,United States(Grant No.RF1AG052653)
文摘The field of functional neuroimaging has substantially advanced as a big data science in the past decade,thanks to international collaborative projects and community efforts.Here 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 methods.The review covers a wide range of literature types including perspectives,database descriptions,methodology developments,and technical details.We 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 community.Furthermore,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.
基金supported by the Open Research Projects of Zhejiang Lab(2021KE0AB04)the Technology Innovation Project of Hubei Province of China(2019AEA171)+1 种基金the National Social Science Foundation of China(19ZDA104 and 20AZD089)the Independent Innovation Research Fund of Huazhong University of Science and Technology(2020WKZDJC004).Author contributions。
文摘Physiological computing uses human physiological data as system inputs in real time.It includes,or significantly overlaps with,brain-computer interfaces,affective computing,adaptive automation,health informatics,and physiological signal based biometrics.Physiological computing increases the communication bandwidth from the user to the computer,but is also subject to various types of adversarial attacks,in which the attacker deliberately manipulates the training and/or test examples to hijack the machine learning algorithm output,leading to possible user confusion,frustration,injury,or even death.However,the vulnerability of physiological computing systems has not been paid enough attention to,and there does not exist a comprehensive review on adversarial attacks to them.This study fills this gap,by providing a systematic review on the main research areas of physiological computing,different types of adversarial attacks and their applications to physiological computing,and the corresponding defense strategies.We hope this review will attract more research interests on the vulnerability of physiological computing systems,and more importantly,defense strategies to make them more secure.
