Smart Healthcare Activity Recognition Using Statistical Regression and Intelligent Learning

In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.

Artificial intelligence:Applications in critical care gastroenterology

Gastrointestinal(GI)complications frequently necessitate intensive care unit(ICU)admission.Additionally,critically ill patients also develop GI complications requiring further diagnostic and therapeutic interventions.However,these patients form a vulnerable group,who are at risk for developing side effects and complications.Every effort must be made to reduce invasiveness and ensure safety of interventions in ICU patients.Artificial intelligence(AI)is a rapidly evolving technology with several potential applications in healthcare settings.ICUs produce a large amount of data,which may be employed for creation of AI algorithms,and provide a lucrative opportunity for application of AI.However,the current role of AI in these patients remains limited due to lack of large-scale trials comparing the efficacy of AI with the accepted standards of care.

Advance IoT Intelligent Healthcare System for Lung Disease Classification Using Ensemble Techniques

In healthcare systems,the Internet of Things(IoT)innovation and development approached new ways to evaluate patient data.A cloud-based platform tends to process data generated by IoT medical devices instead of high storage,and computational hardware.In this paper,an intelligent healthcare system has been proposed for the prediction and severity analysis of lung disease from chest computer tomography(CT)images of patients with pneumonia,Covid-19,tuberculosis(TB),and cancer.Firstly,the CT images are captured and transmitted to the fog node through IoT devices.In the fog node,the image gets modified into a convenient and efficient format for further processing.advanced encryption Standard(AES)algorithm serves a substantial role in IoT and fog nodes for preventing data from being accessed by other operating systems.Finally,the preprocessed image can be classified automatically in the cloud by using various transfer and ensemble learning models.Herein different pre-trained deep learning architectures(Inception-ResNet-v2,VGG-19,ResNet-50)used transfer learning is adopted for feature extraction.The softmax of heterogeneous base classifiers assists to make individual predictions.As a meta-classifier,the ensemble approach is employed to obtain final optimal results.Disease predicted image is consigned to the recurrent neural network with long short-term memory(RNN-LSTM)for severity analysis,and the patient is directed to seek therapy based on the outcome.The proposed method achieved 98.6%accuracy,0.978 precision,0.982 recalls,and 0.974 F1-score on five class classifications.The experimental findings reveal that the proposed framework assists medical experts with lung disease screening and provides a valuable second perspective.

Problems and Countermeasures of Intelligent Elderly Care Service in the Context of Fewer Children in China

With the intensification of population aging and the implementation of the three-child policy,the elderly care pressure of Chinese families continues to rise.Therefore,accelerating the construction of a new intelligent elderly care service model is an important measure to actively respond to population aging,ease the burden of family elderly care and promote high-quality economic development.In view of this,this study analyzed the intelligent elderly care service to explore the relevant countermeasures of the intelligent elderly care service in the context of fewer children.

Aging Characteristics and Layout Planning of Old-age Care Facilities in the Downtown Area of Nanchang City

Compared to the situation of population aging and the elderly aging, the development of oldage undertaking in Nanchang is still undeveloped in that the old-age care industry lags behind the urban renewal speed and the objective needs of aging. Apart from discussions on the composition of elderly population and the status quo of aging, this paper took the institutional old-age care in the downtown area of Nanchang for example, explored problems in the current institutional old-age care facilities through field investigation, and proposed suggestions for the layout planning of old-age care facilities in the study area.

Unveiling significant risk factors for intensive care unit-acquired weakness:Advancing preventive care

In this editorial,we discuss an article titled,“Significant risk factors for intensive care unit-acquired weakness:A processing strategy based on repeated machine learning,”published in a recent issue of the World Journal of Clinical Cases.Intensive care unit-acquired weakness(ICU-AW)is a debilitating condition that affects critically ill patients,with significant implications for patient outcomes and their quality of life.This study explored the use of artificial intelligence and machine learning techniques to predict ICU-AW occurrence and identify key risk factors.Data from a cohort of 1063 adult intensive care unit(ICU)patients were analyzed,with a particular emphasis on variables such as duration of ICU stay,duration of mechanical ventilation,doses of sedatives and vasopressors,and underlying comorbidities.A multilayer perceptron neural network model was developed,which exhibited a remarkable impressive prediction accuracy of 86.2%on the training set and 85.5%on the test set.The study highlights the importance of early prediction and intervention in mitigating ICU-AW risk and improving patient outcomes.

21st century critical care medicine:An overview

Critical care medicine in the 21st century has witnessed remarkable advancements that have significantly improved patient outcomes in intensive care units(ICUs).This abstract provides a concise summary of the latest developments in critical care,highlighting key areas of innovation.Recent advancements in critical care include Precision Medicine:Tailoring treatments based on individual patient characteristics,genomics,and biomarkers to enhance the effectiveness of therapies.The objective is to describe the recent advancements in Critical Care Medicine.Telemedicine:The integration of telehealth technologies for remote patient monitoring and consultation,facilitating timely interventions.Artificial intelligence(AI):AI-driven tools for early disease detection,predictive analytics,and treatment optimization,enhancing clinical decision-making.Organ Support:Advanced life support systems,such as Extracorporeal Membrane Oxygenation and Continuous Renal Replacement Therapy provide better organ support.Infection Control:Innovative infection control measures to combat emerging pathogens and reduce healthcare-associated infections.Ventilation Strategies:Precision ventilation modes and lung-protective strategies to minimize ventilatorinduced lung injury.Sepsis Management:Early recognition and aggressive management of sepsis with tailored interventions.Patient-Centered Care:A shift towards patient-centered care focusing on psychological and emotional wellbeing in addition to medical needs.We conducted a thorough literature search on PubMed,EMBASE,and Scopus using our tailored strategy,incorporating keywords such as critical care,telemedicine,and sepsis management.A total of 125 articles meeting our criteria were included for qualitative synthesis.To ensure reliability,we focused only on articles published in the English language within the last two decades,excluding animal studies,in vitro/molecular studies,and non-original data like editorials,letters,protocols,and conference abstracts.These advancements reflect a dynamic landscape in critical care medicine,where technology,research,and patient-centered approaches converge to improve the quality of care and save lives in ICUs.The future of critical care promises even more innovative solutions to meet the evolving challenges of modern medicine.

Construction of Xi'an ecological old-age care community in the new urbanization

The new urbanization is an important carrier of ecological civilization construction, and ecological old-age care is the concrete manifestation of the perfect combination of the two, the proper meaning of the new urbanization connotation and an inevitable trend of its development. Based on the analysis of the connotation and relationship between the new urbanization and ecological old-age care, together with the current situation and existing problems of the current nursing institutions and ecological old-age care in Xi'an area, this paper discusses the major issues that should be noticed when constructing the ecological old-age care community in Xi'an and three main construction modes.

Artificial Intelligence and Autonomous Machines: Influences, Consequences, and Dilemmas in Human Care

In the field of robotics and in the health sciences, transitions have been occurring in the control of robots operating with predetermined logic and rules. Robotics in health care are influencing human caring dynamics in many ways such as enhancing dependency and surrender to machine technologies. Situations such as these are charged with possibilities of legal liabilities triggered by influences and consequences of advancing robotic technology dependency. The purpose of this paper is to identify, describe, and explain legal issues and/or dilemmas centered on robotics in healthcare while providing engaging opportunities to limit consequent legalities thus forming beneficial human health care outcomes. Laying bare these liabilities will provide critically informative data that can foster proactive encounters which can or may deter health care liabilities while ensuring quality healthcare outcomes. An attempt is made to re-conceptualize how to view agency, causality, liability responsibility, culpability, and autonomy for the new age of autonomous robots. While it is still not clear how this would turn out, a clear framing of the problem is the first step in the project.

Predictive modeling in neurocritical care using causal artificial intelligence

Artificial intelligence(AI)and digital twin models of various systems have long been used in industry to test products quickly and efficiently.Use of digital twins in clinical medicine caught attention with the development of Archimedes,an AI model of diabetes,in 2003.More recently,AI models have been applied to the fields of cardiology,endocrinology,and undergraduate medical education.The use of digital twins and AI thus far has focused mainly on chronic disease management,their application in the field of critical care medicine remains much less explored.In neurocritical care,current AI technology focuses on interpreting electroencephalography,monitoring intracranial pressure,and prognosticating outcomes.AI models have been developed to interpret electroencephalograms by helping to annotate the tracings,detecting seizures,and identifying brain activation in unresponsive patients.In this mini-review we describe the challenges and opportunities in building an actionable AI model pertinent to neurocritical care that can be used to educate the newer generation of clinicians and augment clinical decision making.

An Efficient Blockchain-Based Healthcare System Using Artificial Intelligence

Personal health records and electronic health records are considered as the most sensitive information in the healthcare domain.Several solutions have been provided for implementing the digital health system using blockchain,but there are several challenges,such as secure access control and privacy is one of the prominent issues.Hence,we propose a novel framework and implemented an attribute-based access control system using blockchain.Moreover,we have also integrated artificial intelligence(AI)based approach to identify the behavior and activity for security reasons.The current methods only focus on the related clinical records received from a medical diagnosis.Moreover,existing methods are too inflexible to resourcefully sustenance metadata changes.A secure patient data access framework is proposed in this research,integrating blockchain,trust chain,and blockchain methods to overcome these problems in the literature for sharing and accessing digital healthcare data.We have used a neural network and classifier to categorize the user access to our proposed system.Our proposed scheme provides an intelligent and secure blockchain-based access control system in the digital healthcare system.Experimental results surpass the existing solutions by collecting attributes such as the number of transactions,number of nodes,transaction delay,block creation,and signature verification time.

On the present and future of medical humanistic care in the era of artificial intelligence

With the arrival of the fourth industrial revolution,the combination of artificial intelligence and medical treatment has become an inevitable trend.As a people-centered discipline,the importance of humanistic care is self-evident.Based on the development status of humanistic care,this paper discusses that when artificial intelligence is applied to medicine,it will help patients improve the treatment process,protect their right to know and expression,help medical personnel deal with repetitive work,improve work efficiency,provide doctors with psychological medical time and enhance doctor-patient trust.At the same time,it will inevitably lead to the lack of emotion in the way of diagnosis and treatment,the threat to the authority of doctors,and the disclosure of patients'personal information.Therefore,in the era of artificial intelligence,doctors should return to the service foundation of humanistic care,"master"the"art of medicine"of artificial intelligence technology,and improve the practical road of laws and regulations.

Perioperative nursing care for hip arthroplasty patients with concomitant hypertension: A minireview

Hip replacement(HA)is mainly indicated for the elderly,who generally suffer from various underlying diseases such as hypertension.This article provides a review of the key points of perioperative nursing care for patients with hyper-tension undergoing HA.It analyzes the key points of care during the periop-erative period(preoperative,intraoperative,and postoperative)and proposes directions for the development of perioperative nursing care for HA.The pro-gnosis for patients can be improved through the modification of traditional medical approaches and the application of new technologies and concepts.

Glimpse into the future of prosthodontics:The synergy of artificial intelligence

Prosthodontics,deals in the restoration and replacement of missing and structurally compromised teeth,this field has been remarkably transformed in the last two decades.Through the integration of digital imaging and threedimensional printing,prosthodontics has evolved to provide more durable,precise,and patient-centric outcome.However,as we stand at the convergence of technology and healthcare,a new era is emerging,one that holds immense promise for the field and that is artificial intelligence(AI).In this paper,we explored the fascinating challenges and prospects associated with the future of prosthodontics in the era of AI.

The artificial intelligence evidence-based medicine pyramid

Several studies exist in the literature regarding the exploitation of artificial intelligence in intensive care.However,an important gap between clinical research and daily clinical practice still exists that can only be bridged by robust validation studies carried out by multidisciplinary teams.

Improvement of the Legal System for Addressing the Issue of Elderly Care in China in the Context of Population Aging

Rapid population aging is a social reality facing China at present,and the issue of elderly care has become a hot topic of social concern.Legislation to address the issue of elderly care in the context of population aging should follow systematic concepts to achieve“vertical and horizontal integration.”In terms of content,it is necessary to formulate specific legal approaches around“the elderly and children,”with a focus on guaranteeing the livelihood and protection of the rights of the elderly while taking into account childbirth,employment and other issues.the laws should not only safeguard the social participation and labor rights of the elderly,but also effectively respond to the social challenges brought about by the aging of the population.It is also necessary to optimize the family planning policy to ease the burden of child-raising,improve the population structure and promote the long-term balanced development of the population,thus fundamentally solving the problem of population aging.the effort to improve the legal system to deal with the issue of elderly care in the context of population aging will better advance Chinese modernization.

人工智能养老的伦理困境反思

人工智能养老是人工智能与养老服务相互融合而形成的新兴领域。中国社会正值人口老龄化、高龄化的加速期与上升期,人工智能为满足老年人的健康需求提供前所未有的契机,也面临着严峻的伦理难题和挑战。面向老年人的人工智能伦理治理,应坚持以人为本的价值立场,以增进老年人福祉为价值目标。理念赋能,维护老年人的权利与尊严;关系赋能,倡导多方协作共担责任的伦理治理;技术赋能,弥合老年数字鸿沟;心理赋能,提升老年人内在自我效能。

数字化重症快速反应体系建设探索

目的:建设数字化重症快速反应体系,实现急危重症患者早期识别和干预,保障患者医疗安全。方法:利用物联网、大数据和人工智能等技术,建设重症快速反应信息平台,完善快速反应管理体系。结果:构建急危重症大数据中心、智能预警及快速反应系统、组织管理体系三位一体的重症快速反应体系,为患者提供智慧、同质、高效的医疗服务。结论:重症快速反应体系建设在我国尚处于探索阶段,通过全流程的信息化、数字化管理,切实提高患者医疗安全保障,不断推动构建适合我国医疗高质量发展的重症快速反应体系。

基于文献和知识学习的重症医学大语言模型探索

目的:通过大语言模型技术学习重症医学专科的临床知识,理解和识别临床医疗需求和临床问题意图,并对临床用户提出的问题进行智能反馈。方法:在通用大语言模型的基础上,进行医疗大语言模型数据集的设计,并收集大量的医学知识数据进行多任务多阶段的大语言模型训练,实现对重症医学临床信息的智能分析。结果:实现对重症医学临床医疗信息的智能化理解及自动化问答。结论:应用医疗大语言模型技术,结合临床规则库、医学文献等外部知识体系,可构建重症临床信息大语言模型系统,为临床医疗提供科学、智能的信息理解和决策支持,为大语言模型技术在医疗领域的进一步研究和应用提供经验和参考。