Objective:To systematically sort out the application forms and effects of digital health intervention technologies in oral health management,and provide references for the digital development of stomatology.Methods:By...Objective:To systematically sort out the application forms and effects of digital health intervention technologies in oral health management,and provide references for the digital development of stomatology.Methods:By reviewing relevant domestic and foreign studies and clinical practices,this paper summarizes and analyzes the main application forms of digital health interventions,including digital health education,intelligent detection equipment,telemedicine platforms,oral health big data platforms,and school-hospital collaborative screening robots.Results:Studies have shown that digital health interventions can effectively improve the public’s oral health knowledge level,optimize personal health behaviors,enhance clinical diagnosis efficiency,reduce overall medical costs,and promote the innovation and upgrading of oral health management models.Conclusion:Digital health intervention represents an inevitable trend in the future development of stomatology.In the future,it is still necessary to improve data security and privacy protection,technology adaptability and popularity,as well as relevant policies and norms,to give full play to its potential value.展开更多
BACKGROUND Effective health management for high-risk stroke populations is essential.The hospital-community-home(HCH)collaborative health management(CHM)model leverages resources from hospitals,communities,and familie...BACKGROUND Effective health management for high-risk stroke populations is essential.The hospital-community-home(HCH)collaborative health management(CHM)model leverages resources from hospitals,communities,and families.By integrating patient information across these three domains,it facilitates the delivery of tailored guidance,health risk assessments,and three-in-one health education.AIM To explore the effects of the HCH-CHM model on stroke risk reduction in highrisk populations.METHODS In total,110 high-risk stroke patients screened in the community from January 2019 to January 2023 were enrolled,with 52 patients in the control group receiving routine health education and 58 in the observation group receiving HCH-CHM model interventions based on routine health education.Stroke awareness scores,health behavior levels,medication adherence,blood pressure,serum biochemical markers(systolic/diastolic blood pressure,total cholesterol,and triglyceride),and psychological measures(self-rating anxiety/depression scale)were evaluated and compared between groups.RESULTS The observation group showed statistically significant improvements in stroke awareness scores and health behavior levels compared to the control group(P<0.05),with notable enhancements in lifestyle and dietary habits(P<0.05)and reductions in postintervention systolic blood pressure,diastolic blood pressure,total cholesterol,triglyceride,self-rating anxiety scale,and self-rating depression scale scores(P<0.05).CONCLUSION The HCH-CHM model had a significant positive effect on high-risk stroke populations,effectively increasing disease awareness,improving health behavior and medication adherence,and appropriately ameliorating blood pressure,serum biochemical marker levels,and negative psychological symptoms.展开更多
The digital revolution in agriculture has introduced data-driven decision-making,where artificial intelligence,especially machine learning(ML),helps analyze large and varied data sources to improve soil quality and cr...The digital revolution in agriculture has introduced data-driven decision-making,where artificial intelligence,especially machine learning(ML),helps analyze large and varied data sources to improve soil quality and crop growth indices.Thus,a thorough evaluation of scientific publications from 2007 to 2024 was conducted via the Scopus and Web of Science databases with the PRISMA guidelines to determine the realistic role of ML in soil health and crop improvement under the SDGs.In addition,the present review focused to identify and analyze the trends,challenges,and opportunities associated with the successful implementation of ML in agriculture.The assessment of various databases clearly revealed that ML implementation depends on crop management,while its limited potential in terms of soil health was explored.ML models,such as random forest and XGBoost,have demonstrated high accuracies of up to 99%in crop yield prediction and disease detection.Advanced ML frameworks,including the SHIDS-ADLT and EfficientNetB3,have improved soil health monitoring and plant disease classification.Irrigation management using ML has achieved over 50%water savings and irrigation efficiency by 10%-35%.These findings highlight the potential of ML to improve sustainable agricultural practices and soil health.A significant improvement discussed in this review is AutoML,which simplifies ML model implementation by automating feature selection,model selection,and hyperparameter tuning,reducing dependency on ML expertise.The integration of ML with remote sensing,Internet of Things(IoT),and big data analytics is expected to further transform the precision agriculture and real-time decisionmaking approaches to optimize resource utilization.Conclusively,the present review offers a quantitative perspective on the evolution of ML in agriculture,soil health management,crop yield prediction,and resource optimization.展开更多
Objectives:This study aimed to describe the development process of the peer precision-matching and health management mobile platform"Aspark"for people living with human immunodeficiency virus(HIV)(PLWH)and t...Objectives:This study aimed to describe the development process of the peer precision-matching and health management mobile platform"Aspark"for people living with human immunodeficiency virus(HIV)(PLWH)and to evaluate its usability.Methods:The study assembled a multidisciplinary research and development team comprising researchers,software engineers,art designers,clinical nursing specialists,HIV peer volunteers,and psychological counselors.The team employed an agile development model to iteratively build the platform.Using the social determinants of health framework and the interpersonal circumplex,we identified 14 matching variables for the mobile platform.A video production team was formed to create peer training videos on health management for PLWH.We integrated relevant tools to provide PLWH with various types of support,including informational,instrumental,emotional,affiliational,and appraisal assistance.From March to May 2024,the research team used convenience sampling to select 130 platform users.It administered an electronic questionnaire based on the System Usability Scale to evaluate the platform's overall usability.Results:The platform incorporates multiple functional modules,including"Precision Matching,""Drug Interaction Query,""Medication Reminder,""Health Management,""Mood Diary,""Community Discussion,"and"Activity Center,"achieving comprehensive coverage across informational,emotional,instrumental,social,and appraisal support.The precision matching module facilitates intelligent pairing between patients and peer volunteers;the health management module allows for the entry of test results and trend analysis;medication reminders and check-in features enhance medication adherence;the mood diary supports emotional recording and expressive writing;while the community module offers functions such as topic discussions,appointment scheduling,and organization of offline activities.Among the 130 valid questionnaires,more than 83%of users scored above 68 on the System Usability Scale,indicating that the platform demonstrates favourable usability.Conclusions:The"Aspark"platform offers a feasible and promising digital solution for precision matching in HIV peer support and health management,enabling healthcare providers to deliver personalized and continuous health support to patients through its functions.Well-designed clinical trials should be conducted in the future to evaluate the effectiveness of this tool.展开更多
Objective:To investigate the impact of programmed harmonious nursing combined with hierarchical management on nursing quality and satisfaction in a health management center.Methods:A total of 100 patients who received...Objective:To investigate the impact of programmed harmonious nursing combined with hierarchical management on nursing quality and satisfaction in a health management center.Methods:A total of 100 patients who received care at this health management center from January 2024 to January 2025 were selected as subjects.Using a random number table method,they were divided into an observation group(n=50)and a control group(n=50).The control group followed traditional methods,while the observation group integrated programmed harmonious nursing with hierarchical management.Comparative analysis was conducted on nursing quality scores,adverse event occurrence rates,and patient satisfaction between the two groups.Results:The observation group showed significantly improved nursing quality scores(P<0.05)and markedly reduced incidence of adverse events(P<0.05),with statistically significant differences compared to pre-treatment conditions(P<0.05).Conclusion:The combination of hierarchical management and programmed harmonious nursing demonstrates effectiveness in enhancing medical service quality,reducing adverse reactions,and improving patient satisfaction—a method worthy of promotion.展开更多
Accurate state of health(SOH)estimation is a cornerstone for ensuring the safety,performance and longevity of lithium-ion batteries,especially in electric vehicle(EV)applications.While numerous studies have demonstrat...Accurate state of health(SOH)estimation is a cornerstone for ensuring the safety,performance and longevity of lithium-ion batteries,especially in electric vehicle(EV)applications.While numerous studies have demonstrated the significant advantages of data-driven methods in SOH estimation,most rely on laboratory-standardized test data.This raises concerns about the generalization and robustness of the models under real-world operating conditions,where batteries undergo irregular driving patterns,incomplete charging cycles,and unpredictable environments.Notably,real-world EV data reflects the coupling between battery aging characteristics and actual operating conditions,providing an unprecedented perspective for developing SOH estimation models.This review provides a comprehensive and systematic overview of data-driven SOH estimation using real-world data,a topic that has received increasing attention but lacks a consolidated research framework.The paper begins by reviewing the established SOH estimation methodologies and points out the specific challenges arising from the transition to real-world data.It then probes practical issues across the pipeline:data pre-processing for anomalies,solutions for the lack of labels,feature extraction from complex operating data,machine learning model construction,and performance evaluation across various system deployments.Key insights are presented on how to handle noisy,unlabeled,and heterogeneous data using robust modeling strategies.Moreover,a valuable extension focusing on applying the advancements to battery reuse and recycling is discussed,with the goal of developing a whole lifecycle health diagnosis framework.The paper concludes with promising prospects,encompassing open-source standardized dataset establishment,weakly supervised learning,physics-reinforced modeling,real-world deployment,and advanced sensing technology,emphasizing that real-world data makes the transition of data-driven methods from theoretical validation to industrial deployment promising.This paper aims to assist researchers and practitioners in navigating the complexities of real-world SOH estimation,accelerating the collaborative innovation and industrial adoption in battery health management.展开更多
Objective: To explore the application effect of optimizing the nursing model in community health centers for elderly health examinations, providing a reference for enhancing the management level of elderly health at t...Objective: To explore the application effect of optimizing the nursing model in community health centers for elderly health examinations, providing a reference for enhancing the management level of elderly health at the grassroots level. Methods: A total of 300 elderly individuals who underwent health examinations at our center from January 2024 to December 2024 were selected as the study subjects. They were randomly divided into a control group and an observation group, with 150 cases in each group. The control group underwent the conventional health examination nursing process, while the observation group adopted an optimized community nursing model, which included stratified education and appointment scheduling before the examination, full-time accompaniment and safety care during the examination, and the establishment of electronic medical records and continuous follow-up after the examination. Differences in health management awareness rates and satisfaction with health examination services between the two groups of elderly individuals were compared. Results: The health management awareness rate in the observation group was 94.67%, significantly higher than that in the control group (78.00%;p < 0.001). The satisfaction rate with health examination services in the observation group was 96.00%, also significantly higher than that in the control group (82.00%;p < 0.001). Conclusion: The optimized community nursing model can effectively enhance the health awareness level and service satisfaction of elderly individuals during health examinations, demonstrating strong practicality and promotion value. It contributes to achieving continuity and precision in grassroots elderly health management.展开更多
Objective: The health-related quality of life (HRQoL) theory was used to assess the health quality of elderly residents in Southwest China. This was done by using the European Five-Dimensional Health Scale in a compre...Objective: The health-related quality of life (HRQoL) theory was used to assess the health quality of elderly residents in Southwest China. This was done by using the European Five-Dimensional Health Scale in a comprehensive manner and by analysing the factors influencing it. The findings will provide new perspectives and ideas for improving the health-related quality of life of the elderly population and enhancing the precise health management of elderly residents. Methods: The response data of 1892 elderly residents in southwestern China were included in the analysis based on the CLHLS data. The factors influencing the occurrence of problems, EQ-VAS scores and health utility values were analysed by logistic regression, multiple linear regression and Tobit regression, respectively. Results: The primary health concerns among the elderly population in the Southwest region were limited ability to perform daily activities and pain or discomfort. These individuals exhibited an EQ-VAS self-assessment score of 66.51 ± 14.87 and a health utility value of 0.87 (0.70, 1.00). Gender, age, regular medical check-ups, exercise habits and the prevalence of chronic diseases are the main influencing factors. Conclusions: The health quality of elderly people in Southwest China needs to be improved, and a comprehensive management strategy can be adopted in terms of lifestyle management, health needs management and disease management to improve the quality of their healthy lives and promote the development of healthy ageing.展开更多
Objective: To analyze the clinical effects of the patient participation health model in the health management of type 2 diabetes mellitus. Methods: A total of 124 patients with type 2 diabetes admitted to the hospital...Objective: To analyze the clinical effects of the patient participation health model in the health management of type 2 diabetes mellitus. Methods: A total of 124 patients with type 2 diabetes admitted to the hospital from June 2023 to June 2024 were randomly assigned to either the control group (64 patients) or the intervention group (60 patients). Patients in the control group received routine health management, while those in the intervention group were managed using a patient-participation health model with progressive, stage-based interventions. Outcomes were assessed based on blood glucose control, disease awareness, and self-management behaviors. Adverse reactions during health management were closely monitored in both groups. Results: Patients in the intervention group showed significantly better outcomes in blood glucose control, disease awareness, and self-management behaviors compared to the control group. Conclusion: The patient participation health model demonstrated significant clinical value, effectively enhancing self-management abilities, improving glycemic control, and increasing disease awareness. This model is recommended for widespread adoption in the health management of type 2 diabetes to achieve better therapeutic outcomes and improve patient quality of life.展开更多
[Objectives] To investigate and analyze the physical examination status of the staff in an agricultural university in Tianjin,to understand the composition of the diseases suffered by the staff in the university,and t...[Objectives] To investigate and analyze the physical examination status of the staff in an agricultural university in Tianjin,to understand the composition of the diseases suffered by the staff in the university,and to provide the basis for the formulation of health education programs and health management countermeasures.[Methods] A comprehensive physical examination survey was conducted among 377 teachers and staff in the school,and the results were statistically analyzed by SPSS 24.0.[Results] The detection rates of diseases among the staff of a university in Tianjin were 161(42.7%)overweight and obesity,167(44.3%)dyslipidemia,250(66.31%)thyroid nodules,214(56.76%)fatty liver,116(30.77%)hyperglycemia,96(25.46%)hypertension,90 cases(23.87%)had abnormal ECG,and the prevalence rate increased with age.[Conclusions] The health status of the staff in a university in Tianjin is not optimistic.They should strengthen health education and health care guidance,increase sports and physical exercise,actively do a good job in early diagnosis,treatment and prevention of diseases,and improve their health level.展开更多
At the current stage,China’s medical and health industry is constantly developing,and it is essential to fully attach importance to the core role of Centers for Disease Control and Prevention(CDC).For tertiary hospit...At the current stage,China’s medical and health industry is constantly developing,and it is essential to fully attach importance to the core role of Centers for Disease Control and Prevention(CDC).For tertiary hospitals,one of their key work tasks is to carry out public health management.Based on this,this paper mainly expounds on some basic problems existing in the public health management of state-owned hospitals at the current stage,and puts forward corresponding countermeasures for the identified problems.These countermeasures include enhancing the emphasis on public health management,strengthening the intensity of medical management,effectively improving the allocation of medical management resources,and intensifying the training of relevant personnel.This paper aims to provide references for improving the level of hospital public health management.展开更多
Background:The historical trajectory of TCM appropriate technologies spans from their primordial origins in daily life practices,through the systematization of classical theoretical frameworks,to modern scientific val...Background:The historical trajectory of TCM appropriate technologies spans from their primordial origins in daily life practices,through the systematization of classical theoretical frameworks,to modern scientific validation and health management applications.Methods:Employing literature review and historical analysis methodologies,this study systematically traces and synthesizes the entire developmental pathway of TCM appropriate technologies.Results:The analysis reveals the intrinsic developmental patterns of these technologies and underscores their practical significance in“preventive treatment”and chronic condition management.A key exemplar of modern innovation is presented:the Hand Twelve Jing-Well Points Exercise.This non-invasive technique adapts the classical Jing-Well Points(jǐng xué)theory,extending its traditional functions in emergency care and brain health from a focus on“treating established diseases”to the paradigm of“preventive treatment.”Conclusion:This research provides a comprehensive case study for the systematic investigation of TCM appropriate technologies.It elucidates a model of inheritance and innovation within modern healthcare systems and offers a theoretical foundation for the promotion and cultural dissemination of these practices.展开更多
Health management permits the reliability of a system and plays a increasingly important role for achieving efficient system-level maintenance.It has been used for remaining useful life（RUL） prognostics of electroni...Health management permits the reliability of a system and plays a increasingly important role for achieving efficient system-level maintenance.It has been used for remaining useful life（RUL） prognostics of electronics-rich system including avionics.Prognostics and health management（PHM） have become highly desirable to provide avionics with system level health management.This paper presents a health management and fusion prognostic model for avionics system,combining three baseline prognostic approaches that are model-based,data-driven and knowledge-based approaches,and integrates merits as well as eliminates some limitations of each single approach to achieve fusion prognostics and improved prognostic performance of RUL estimation.A fusion model built upon an optimal linear combination forecast model is then utilized to fuse single prognostic algorithm representing the three baseline approaches correspondingly,and the presented case study shows that the fusion prognostics can provide RUL estimation more accurate and more robust than either algorithm alone.展开更多
BACKGROUND Epidemiological studies have found that the prevalence of chronic heart failure in China is 0.9%,the number of people affected is more than 4 million,and the 5-year survival rate is even lower than that of ...BACKGROUND Epidemiological studies have found that the prevalence of chronic heart failure in China is 0.9%,the number of people affected is more than 4 million,and the 5-year survival rate is even lower than that of malignant tumors.AIM To determine the impact of WeChat platform-based health management on severe chronic heart failure patients’health and self-management efficacy.METHODS A total of 120 patients suffering from chronic heart failure with cardiac function grade III-IV,under the classification of the New York Heart Association,were admitted to our hospital in May 2017.In January 2020,they were divided into two groups:A control group(with routine nursing intervention)and an observation group(with WeChat platform-based health management intervention).Changes in cardiac function,6-min walking distance(6MWD),high-sensitivity cardiac troponin(hs-cTnT),and N-terminal pro B-type natriuretic peptide(NT-proBNP)were detected in both groups.The Self-Care Ability Scale(ESCA)score,Minnesota Living with Heart Failure Questionnaire score,and compliance score were used to evaluate self-management ability,quality of life,and compliance of the two groups.During a follow-up period of 12 mo,the occurrence of cardiovascular adverse events in both the groups was counted.RESULTS The left ventricular ejection fraction,stroke output,and 6MWD increased,and the hs-cTnT and NT-proBNP decreased in both the groups,as compared to those before the intervention.Further,cardiac function during the 6MWD,hs-cTnT,and NT-proBNP improved significantly in the observation group after intervention(P<0.05).The scores of self-care responsibility,self-concept,self-care skills,and selfcare health knowledge in the observation group were higher than those of the control group before intervention,and their ESCA scores were significantly improved after intervention(P<0.05).The Minnesota heart failure quality of life(LiHFe)scores of physical restriction,disease symptoms,psychological emotion,social relations,and other items were decreased compared to those of the control group before intervention,and the LiHFe scores of the observation group were significantly improved compared to those of the control group(P<0.05).With intervention,the compliance scores of rational diet,regular medication,healthy behavior,and timely reexamination were increased,thereby leading to the compliance scores of the observation group being significantly improved compared to those of the control group(P<0.05).During the 12 mo follow-up,the incidence rates of acute myocardial infarction and cardiogenic rehospitalization in the observation group were lower than those of the control group,and the hospitalization time in the observation group was shorter than that of the control group,but there was no significant difference between the two groups(P>0.05).CONCLUSION WeChat platform-based health management can improve the self-care ability and compliance of patients with severe chronic heart failure,improve the cardiac function and related indexes,reduce the occurrence of cardiovascular adverse events,and enable the avoidance of rehospitalization.展开更多
Prognostics and health management （PHM） is very important to guarantee the reliability and safety of aerospace systems, and sensing and test are the precondition of PHM. Integrating design for testability into early...Prognostics and health management （PHM） is very important to guarantee the reliability and safety of aerospace systems, and sensing and test are the precondition of PHM. Integrating design for testability into early design stage of system early design stage is deemed as a fundamental way to improve PHM performance, and testability model is the base of testability analysis and design. This paper discusses a hierarchical model-based approach to testability modeling and analysis for heading attitude system health management. Quantified directed graph, of which the nodes represent components and tests and the directed edges represent fault propagation paths, is used to describe fault-test dependency, and quantitative testability information is assigned to nodes and directed edges. The fault dependencies between nodes can be obtained by functional fault analysis methodology that captures the physical architecture and material flows such as energy, heat, data, and so on. By incorporating physics of failure models into component, the dynamic process of a failing or degrading component can be projected onto system behavior, i.e., system symptoms. Then, the analysis of extended failure modes, mechanisms and effects is utilized to construct fault evolution-test dependency. Using this integrated model, the designers and system analysts can assess the test suite＇s fault detectability, fault isolability and fault predictability. And heading attitude system application results show that the proposed model can support testability analysis and design for PHM very well.展开更多
State of health(SoH) estimation plays a key role in smart battery health prognostic and management.However,poor generalization,lack of labeled data,and unused measurements during aging are still major challenges to ac...State of health(SoH) estimation plays a key role in smart battery health prognostic and management.However,poor generalization,lack of labeled data,and unused measurements during aging are still major challenges to accurate SoH estimation.Toward this end,this paper proposes a self-supervised learning framework to boost the performance of battery SoH estimation.Different from traditional data-driven methods which rely on a considerable training dataset obtained from numerous battery cells,the proposed method achieves accurate and robust estimations using limited labeled data.A filter-based data preprocessing technique,which enables the extraction of partial capacity-voltage curves under dynamic charging profiles,is applied at first.Unsupervised learning is then used to learn the aging characteristics from the unlabeled data through an auto-encoder-decoder.The learned network parameters are transferred to the downstream SoH estimation task and are fine-tuned with very few sparsely labeled data,which boosts the performance of the estimation framework.The proposed method has been validated under different battery chemistries,formats,operating conditions,and ambient.The estimation accuracy can be guaranteed by using only three labeled data from the initial 20% life cycles,with overall errors less than 1.14% and error distribution of all testing scenarios maintaining less than 4%,and robustness increases with aging.Comparisons with other pure supervised machine learning methods demonstrate the superiority of the proposed method.This simple and data-efficient estimation framework is promising in real-world applications under a variety of scenarios.展开更多
Current research on Digital Twin(DT)based Prognostics and Health Management(PHM)focuses on establishment of DT through integration of real-time data from various sources to facilitate comprehensive product monitoring ...Current research on Digital Twin(DT)based Prognostics and Health Management(PHM)focuses on establishment of DT through integration of real-time data from various sources to facilitate comprehensive product monitoring and health management.However,there still exist gaps in the seamless integration of DT and PHM,as well as in the development of DT multi-field coupling modeling and its dynamic update mechanism.When the product experiences long-period degradation under load spectrum,it is challenging to describe the dynamic evolution of the health status and degradation progression accurately.In addition,DT update algorithms are difficult to be integrated simultaneously by current methods.This paper proposes an innovative dual loop DT based PHM framework,in which the first loop establishes the basic dynamic DT with multi-filed coupling,and the second loop implements the PHM and the abnormal detection to provide the interaction between the dual loops through updating mechanism.The proposed method pays attention to the internal state changes with degradation and interactive mapping with dynamic parameter updating.Furthermore,the Independence Principle for the abnormal detection is proposed to refine the theory of DT.Events at the first loop focus on accurate modeling of multi-field coupling,while the events at the second loop focus on real-time occurrence of anomalies and the product degradation trend.The interaction and collaboration between different loop models are also discussed.Finally,the Permanent Magnet Synchronous Motor(PMSM)is used to verify the proposed method.The results show that the modeling method proposed can accurately track the lifecycle performance changes of the entity and carry out remaining life prediction and health management effectively.展开更多
Testing is the premise and foundation of realizing equipment health management （EHM）. To address the problem that the static periodic test strategy may cause deficient test or excessive test, a dynamic sequential te...Testing is the premise and foundation of realizing equipment health management （EHM）. To address the problem that the static periodic test strategy may cause deficient test or excessive test, a dynamic sequential test strategy （DSTS） for EHM is presented. Considering the situation that equipment health state is not completely observable in reality, a DSTS optimization method based on partially observable semi-Markov decision pro- cess （POSMDP） is proposed. Firstly, an equipment health state degradation model is constructed by Markov process, and the control limit maintenance policy is also introduced. Secondly, POSMDP is formulated in great detail. And then, POSMDP is converted to completely observable belief semi-Markov decision process （BSMDP） through belief state. The optimal equation and the corresponding optimal DSTS, which minimize the long-run ex- pected average cost per unit time, are obtained with BSMDP. The results of application in complex equipment show that the proposed DSTS is feasible and effective.展开更多
The prognostics health management(PHM)fromthe systematic viewis critical to the healthy continuous operation of processmanufacturing systems(PMS),with different kinds of dynamic interference events.This paper proposes...The prognostics health management(PHM)fromthe systematic viewis critical to the healthy continuous operation of processmanufacturing systems(PMS),with different kinds of dynamic interference events.This paper proposes a three leveled digital twinmodel for the systematic PHMof PMSs.The unit-leveled digital twinmodel of each basic device unit of PMSs is constructed based on edge computing,which can provide real-time monitoring and analysis of the device status.The station-leveled digital twin models in the PMSs are designed to optimize and control the process parameters,which are deployed for the manufacturing execution on the fog server.The shop-leveled digital twin maintenancemodel is designed for production planning,which gives production instructions fromthe private industrial cloud server.To cope with the dynamic disturbances of a PMS,a big data-driven framework is proposed to control the three-level digital twin models,which contains indicator prediction,influence evaluation,and decisionmaking.Finally,a case study with a real chemical fiber system is introduced to illustrate the effectiveness of the digital twin model with edge-fog-cloud computing for the systematic PHM of PMSs.The result demonstrates that the three-leveled digital twin model for the systematic PHM in PMSs works well in the system’s respects.展开更多
The growing demand for health management puts forward high requirements for the quality of health knowledge.A content-based method is proposed to address the current demand for high-quality health knowledge,which eval...The growing demand for health management puts forward high requirements for the quality of health knowledge.A content-based method is proposed to address the current demand for high-quality health knowledge,which evaluates the quality including the certainty,accuracy,and operability of different types of knowledge from the perspectives of authority,precision,and information entropy.Herein,the health knowledge of myocardial infarction is used as an example,and knowledge is first classified into different types and then evaluated.This method is applied to knowledge in the existing health management system and it can support knowledge screening and comparison under the cold start condition.Compared with the current evaluation methods based on knowledge use behavior and utility,the new evaluation method provides a reference for evaluation when the knowledge is first used.The screening of high quality knowledge can help the subsequent application of knowledge and improve user’s compliance.Concurrently,the arrangement of myocardial infarction knowledge can also provide a knowledge reference for patients’daily health management.展开更多
文摘Objective:To systematically sort out the application forms and effects of digital health intervention technologies in oral health management,and provide references for the digital development of stomatology.Methods:By reviewing relevant domestic and foreign studies and clinical practices,this paper summarizes and analyzes the main application forms of digital health interventions,including digital health education,intelligent detection equipment,telemedicine platforms,oral health big data platforms,and school-hospital collaborative screening robots.Results:Studies have shown that digital health interventions can effectively improve the public’s oral health knowledge level,optimize personal health behaviors,enhance clinical diagnosis efficiency,reduce overall medical costs,and promote the innovation and upgrading of oral health management models.Conclusion:Digital health intervention represents an inevitable trend in the future development of stomatology.In the future,it is still necessary to improve data security and privacy protection,technology adaptability and popularity,as well as relevant policies and norms,to give full play to its potential value.
基金Supported by Guiding Project of Hebei Provincial Health Commission,No.20201190 and 20180220.
文摘BACKGROUND Effective health management for high-risk stroke populations is essential.The hospital-community-home(HCH)collaborative health management(CHM)model leverages resources from hospitals,communities,and families.By integrating patient information across these three domains,it facilitates the delivery of tailored guidance,health risk assessments,and three-in-one health education.AIM To explore the effects of the HCH-CHM model on stroke risk reduction in highrisk populations.METHODS In total,110 high-risk stroke patients screened in the community from January 2019 to January 2023 were enrolled,with 52 patients in the control group receiving routine health education and 58 in the observation group receiving HCH-CHM model interventions based on routine health education.Stroke awareness scores,health behavior levels,medication adherence,blood pressure,serum biochemical markers(systolic/diastolic blood pressure,total cholesterol,and triglyceride),and psychological measures(self-rating anxiety/depression scale)were evaluated and compared between groups.RESULTS The observation group showed statistically significant improvements in stroke awareness scores and health behavior levels compared to the control group(P<0.05),with notable enhancements in lifestyle and dietary habits(P<0.05)and reductions in postintervention systolic blood pressure,diastolic blood pressure,total cholesterol,triglyceride,self-rating anxiety scale,and self-rating depression scale scores(P<0.05).CONCLUSION The HCH-CHM model had a significant positive effect on high-risk stroke populations,effectively increasing disease awareness,improving health behavior and medication adherence,and appropriately ameliorating blood pressure,serum biochemical marker levels,and negative psychological symptoms.
基金supported by the Ministry of Science and Higher Education of the Russian Federation(no.FENW-2023-0008)the Strategic Academic Leadership Program of Southern Federal University,known as“Priority 2030”.
文摘The digital revolution in agriculture has introduced data-driven decision-making,where artificial intelligence,especially machine learning(ML),helps analyze large and varied data sources to improve soil quality and crop growth indices.Thus,a thorough evaluation of scientific publications from 2007 to 2024 was conducted via the Scopus and Web of Science databases with the PRISMA guidelines to determine the realistic role of ML in soil health and crop improvement under the SDGs.In addition,the present review focused to identify and analyze the trends,challenges,and opportunities associated with the successful implementation of ML in agriculture.The assessment of various databases clearly revealed that ML implementation depends on crop management,while its limited potential in terms of soil health was explored.ML models,such as random forest and XGBoost,have demonstrated high accuracies of up to 99%in crop yield prediction and disease detection.Advanced ML frameworks,including the SHIDS-ADLT and EfficientNetB3,have improved soil health monitoring and plant disease classification.Irrigation management using ML has achieved over 50%water savings and irrigation efficiency by 10%-35%.These findings highlight the potential of ML to improve sustainable agricultural practices and soil health.A significant improvement discussed in this review is AutoML,which simplifies ML model implementation by automating feature selection,model selection,and hyperparameter tuning,reducing dependency on ML expertise.The integration of ML with remote sensing,Internet of Things(IoT),and big data analytics is expected to further transform the precision agriculture and real-time decisionmaking approaches to optimize resource utilization.Conclusively,the present review offers a quantitative perspective on the evolution of ML in agriculture,soil health management,crop yield prediction,and resource optimization.
基金funded by the National Natural Science Foundation of China(72204006).
文摘Objectives:This study aimed to describe the development process of the peer precision-matching and health management mobile platform"Aspark"for people living with human immunodeficiency virus(HIV)(PLWH)and to evaluate its usability.Methods:The study assembled a multidisciplinary research and development team comprising researchers,software engineers,art designers,clinical nursing specialists,HIV peer volunteers,and psychological counselors.The team employed an agile development model to iteratively build the platform.Using the social determinants of health framework and the interpersonal circumplex,we identified 14 matching variables for the mobile platform.A video production team was formed to create peer training videos on health management for PLWH.We integrated relevant tools to provide PLWH with various types of support,including informational,instrumental,emotional,affiliational,and appraisal assistance.From March to May 2024,the research team used convenience sampling to select 130 platform users.It administered an electronic questionnaire based on the System Usability Scale to evaluate the platform's overall usability.Results:The platform incorporates multiple functional modules,including"Precision Matching,""Drug Interaction Query,""Medication Reminder,""Health Management,""Mood Diary,""Community Discussion,"and"Activity Center,"achieving comprehensive coverage across informational,emotional,instrumental,social,and appraisal support.The precision matching module facilitates intelligent pairing between patients and peer volunteers;the health management module allows for the entry of test results and trend analysis;medication reminders and check-in features enhance medication adherence;the mood diary supports emotional recording and expressive writing;while the community module offers functions such as topic discussions,appointment scheduling,and organization of offline activities.Among the 130 valid questionnaires,more than 83%of users scored above 68 on the System Usability Scale,indicating that the platform demonstrates favourable usability.Conclusions:The"Aspark"platform offers a feasible and promising digital solution for precision matching in HIV peer support and health management,enabling healthcare providers to deliver personalized and continuous health support to patients through its functions.Well-designed clinical trials should be conducted in the future to evaluate the effectiveness of this tool.
文摘Objective:To investigate the impact of programmed harmonious nursing combined with hierarchical management on nursing quality and satisfaction in a health management center.Methods:A total of 100 patients who received care at this health management center from January 2024 to January 2025 were selected as subjects.Using a random number table method,they were divided into an observation group(n=50)and a control group(n=50).The control group followed traditional methods,while the observation group integrated programmed harmonious nursing with hierarchical management.Comparative analysis was conducted on nursing quality scores,adverse event occurrence rates,and patient satisfaction between the two groups.Results:The observation group showed significantly improved nursing quality scores(P<0.05)and markedly reduced incidence of adverse events(P<0.05),with statistically significant differences compared to pre-treatment conditions(P<0.05).Conclusion:The combination of hierarchical management and programmed harmonious nursing demonstrates effectiveness in enhancing medical service quality,reducing adverse reactions,and improving patient satisfaction—a method worthy of promotion.
基金supported by the National Natural Science Foundation of China(52375144 and 52205153)the Shanghai Pujiang Programme(23PJD019)the Shanghai Gaofeng Project for University Academic Program Development。
文摘Accurate state of health(SOH)estimation is a cornerstone for ensuring the safety,performance and longevity of lithium-ion batteries,especially in electric vehicle(EV)applications.While numerous studies have demonstrated the significant advantages of data-driven methods in SOH estimation,most rely on laboratory-standardized test data.This raises concerns about the generalization and robustness of the models under real-world operating conditions,where batteries undergo irregular driving patterns,incomplete charging cycles,and unpredictable environments.Notably,real-world EV data reflects the coupling between battery aging characteristics and actual operating conditions,providing an unprecedented perspective for developing SOH estimation models.This review provides a comprehensive and systematic overview of data-driven SOH estimation using real-world data,a topic that has received increasing attention but lacks a consolidated research framework.The paper begins by reviewing the established SOH estimation methodologies and points out the specific challenges arising from the transition to real-world data.It then probes practical issues across the pipeline:data pre-processing for anomalies,solutions for the lack of labels,feature extraction from complex operating data,machine learning model construction,and performance evaluation across various system deployments.Key insights are presented on how to handle noisy,unlabeled,and heterogeneous data using robust modeling strategies.Moreover,a valuable extension focusing on applying the advancements to battery reuse and recycling is discussed,with the goal of developing a whole lifecycle health diagnosis framework.The paper concludes with promising prospects,encompassing open-source standardized dataset establishment,weakly supervised learning,physics-reinforced modeling,real-world deployment,and advanced sensing technology,emphasizing that real-world data makes the transition of data-driven methods from theoretical validation to industrial deployment promising.This paper aims to assist researchers and practitioners in navigating the complexities of real-world SOH estimation,accelerating the collaborative innovation and industrial adoption in battery health management.
文摘Objective: To explore the application effect of optimizing the nursing model in community health centers for elderly health examinations, providing a reference for enhancing the management level of elderly health at the grassroots level. Methods: A total of 300 elderly individuals who underwent health examinations at our center from January 2024 to December 2024 were selected as the study subjects. They were randomly divided into a control group and an observation group, with 150 cases in each group. The control group underwent the conventional health examination nursing process, while the observation group adopted an optimized community nursing model, which included stratified education and appointment scheduling before the examination, full-time accompaniment and safety care during the examination, and the establishment of electronic medical records and continuous follow-up after the examination. Differences in health management awareness rates and satisfaction with health examination services between the two groups of elderly individuals were compared. Results: The health management awareness rate in the observation group was 94.67%, significantly higher than that in the control group (78.00%;p < 0.001). The satisfaction rate with health examination services in the observation group was 96.00%, also significantly higher than that in the control group (82.00%;p < 0.001). Conclusion: The optimized community nursing model can effectively enhance the health awareness level and service satisfaction of elderly individuals during health examinations, demonstrating strong practicality and promotion value. It contributes to achieving continuity and precision in grassroots elderly health management.
文摘Objective: The health-related quality of life (HRQoL) theory was used to assess the health quality of elderly residents in Southwest China. This was done by using the European Five-Dimensional Health Scale in a comprehensive manner and by analysing the factors influencing it. The findings will provide new perspectives and ideas for improving the health-related quality of life of the elderly population and enhancing the precise health management of elderly residents. Methods: The response data of 1892 elderly residents in southwestern China were included in the analysis based on the CLHLS data. The factors influencing the occurrence of problems, EQ-VAS scores and health utility values were analysed by logistic regression, multiple linear regression and Tobit regression, respectively. Results: The primary health concerns among the elderly population in the Southwest region were limited ability to perform daily activities and pain or discomfort. These individuals exhibited an EQ-VAS self-assessment score of 66.51 ± 14.87 and a health utility value of 0.87 (0.70, 1.00). Gender, age, regular medical check-ups, exercise habits and the prevalence of chronic diseases are the main influencing factors. Conclusions: The health quality of elderly people in Southwest China needs to be improved, and a comprehensive management strategy can be adopted in terms of lifestyle management, health needs management and disease management to improve the quality of their healthy lives and promote the development of healthy ageing.
文摘Objective: To analyze the clinical effects of the patient participation health model in the health management of type 2 diabetes mellitus. Methods: A total of 124 patients with type 2 diabetes admitted to the hospital from June 2023 to June 2024 were randomly assigned to either the control group (64 patients) or the intervention group (60 patients). Patients in the control group received routine health management, while those in the intervention group were managed using a patient-participation health model with progressive, stage-based interventions. Outcomes were assessed based on blood glucose control, disease awareness, and self-management behaviors. Adverse reactions during health management were closely monitored in both groups. Results: Patients in the intervention group showed significantly better outcomes in blood glucose control, disease awareness, and self-management behaviors compared to the control group. Conclusion: The patient participation health model demonstrated significant clinical value, effectively enhancing self-management abilities, improving glycemic control, and increasing disease awareness. This model is recommended for widespread adoption in the health management of type 2 diabetes to achieve better therapeutic outcomes and improve patient quality of life.
文摘[Objectives] To investigate and analyze the physical examination status of the staff in an agricultural university in Tianjin,to understand the composition of the diseases suffered by the staff in the university,and to provide the basis for the formulation of health education programs and health management countermeasures.[Methods] A comprehensive physical examination survey was conducted among 377 teachers and staff in the school,and the results were statistically analyzed by SPSS 24.0.[Results] The detection rates of diseases among the staff of a university in Tianjin were 161(42.7%)overweight and obesity,167(44.3%)dyslipidemia,250(66.31%)thyroid nodules,214(56.76%)fatty liver,116(30.77%)hyperglycemia,96(25.46%)hypertension,90 cases(23.87%)had abnormal ECG,and the prevalence rate increased with age.[Conclusions] The health status of the staff in a university in Tianjin is not optimistic.They should strengthen health education and health care guidance,increase sports and physical exercise,actively do a good job in early diagnosis,treatment and prevention of diseases,and improve their health level.
文摘At the current stage,China’s medical and health industry is constantly developing,and it is essential to fully attach importance to the core role of Centers for Disease Control and Prevention(CDC).For tertiary hospitals,one of their key work tasks is to carry out public health management.Based on this,this paper mainly expounds on some basic problems existing in the public health management of state-owned hospitals at the current stage,and puts forward corresponding countermeasures for the identified problems.These countermeasures include enhancing the emphasis on public health management,strengthening the intensity of medical management,effectively improving the allocation of medical management resources,and intensifying the training of relevant personnel.This paper aims to provide references for improving the level of hospital public health management.
基金supported by the Tianjin Municipal Education Commission Scientific Research Plan Project(Grant No.2022ZD042).
文摘Background:The historical trajectory of TCM appropriate technologies spans from their primordial origins in daily life practices,through the systematization of classical theoretical frameworks,to modern scientific validation and health management applications.Methods:Employing literature review and historical analysis methodologies,this study systematically traces and synthesizes the entire developmental pathway of TCM appropriate technologies.Results:The analysis reveals the intrinsic developmental patterns of these technologies and underscores their practical significance in“preventive treatment”and chronic condition management.A key exemplar of modern innovation is presented:the Hand Twelve Jing-Well Points Exercise.This non-invasive technique adapts the classical Jing-Well Points(jǐng xué)theory,extending its traditional functions in emergency care and brain health from a focus on“treating established diseases”to the paradigm of“preventive treatment.”Conclusion:This research provides a comprehensive case study for the systematic investigation of TCM appropriate technologies.It elucidates a model of inheritance and innovation within modern healthcare systems and offers a theoretical foundation for the promotion and cultural dissemination of these practices.
文摘Health management permits the reliability of a system and plays a increasingly important role for achieving efficient system-level maintenance.It has been used for remaining useful life（RUL） prognostics of electronics-rich system including avionics.Prognostics and health management（PHM） have become highly desirable to provide avionics with system level health management.This paper presents a health management and fusion prognostic model for avionics system,combining three baseline prognostic approaches that are model-based,data-driven and knowledge-based approaches,and integrates merits as well as eliminates some limitations of each single approach to achieve fusion prognostics and improved prognostic performance of RUL estimation.A fusion model built upon an optimal linear combination forecast model is then utilized to fuse single prognostic algorithm representing the three baseline approaches correspondingly,and the presented case study shows that the fusion prognostics can provide RUL estimation more accurate and more robust than either algorithm alone.
文摘BACKGROUND Epidemiological studies have found that the prevalence of chronic heart failure in China is 0.9%,the number of people affected is more than 4 million,and the 5-year survival rate is even lower than that of malignant tumors.AIM To determine the impact of WeChat platform-based health management on severe chronic heart failure patients’health and self-management efficacy.METHODS A total of 120 patients suffering from chronic heart failure with cardiac function grade III-IV,under the classification of the New York Heart Association,were admitted to our hospital in May 2017.In January 2020,they were divided into two groups:A control group(with routine nursing intervention)and an observation group(with WeChat platform-based health management intervention).Changes in cardiac function,6-min walking distance(6MWD),high-sensitivity cardiac troponin(hs-cTnT),and N-terminal pro B-type natriuretic peptide(NT-proBNP)were detected in both groups.The Self-Care Ability Scale(ESCA)score,Minnesota Living with Heart Failure Questionnaire score,and compliance score were used to evaluate self-management ability,quality of life,and compliance of the two groups.During a follow-up period of 12 mo,the occurrence of cardiovascular adverse events in both the groups was counted.RESULTS The left ventricular ejection fraction,stroke output,and 6MWD increased,and the hs-cTnT and NT-proBNP decreased in both the groups,as compared to those before the intervention.Further,cardiac function during the 6MWD,hs-cTnT,and NT-proBNP improved significantly in the observation group after intervention(P<0.05).The scores of self-care responsibility,self-concept,self-care skills,and selfcare health knowledge in the observation group were higher than those of the control group before intervention,and their ESCA scores were significantly improved after intervention(P<0.05).The Minnesota heart failure quality of life(LiHFe)scores of physical restriction,disease symptoms,psychological emotion,social relations,and other items were decreased compared to those of the control group before intervention,and the LiHFe scores of the observation group were significantly improved compared to those of the control group(P<0.05).With intervention,the compliance scores of rational diet,regular medication,healthy behavior,and timely reexamination were increased,thereby leading to the compliance scores of the observation group being significantly improved compared to those of the control group(P<0.05).During the 12 mo follow-up,the incidence rates of acute myocardial infarction and cardiogenic rehospitalization in the observation group were lower than those of the control group,and the hospitalization time in the observation group was shorter than that of the control group,but there was no significant difference between the two groups(P>0.05).CONCLUSION WeChat platform-based health management can improve the self-care ability and compliance of patients with severe chronic heart failure,improve the cardiac function and related indexes,reduce the occurrence of cardiovascular adverse events,and enable the avoidance of rehospitalization.
基金supported by National Natural Science Foundation of China (No. 51175502)
文摘Prognostics and health management （PHM） is very important to guarantee the reliability and safety of aerospace systems, and sensing and test are the precondition of PHM. Integrating design for testability into early design stage of system early design stage is deemed as a fundamental way to improve PHM performance, and testability model is the base of testability analysis and design. This paper discusses a hierarchical model-based approach to testability modeling and analysis for heading attitude system health management. Quantified directed graph, of which the nodes represent components and tests and the directed edges represent fault propagation paths, is used to describe fault-test dependency, and quantitative testability information is assigned to nodes and directed edges. The fault dependencies between nodes can be obtained by functional fault analysis methodology that captures the physical architecture and material flows such as energy, heat, data, and so on. By incorporating physics of failure models into component, the dynamic process of a failing or degrading component can be projected onto system behavior, i.e., system symptoms. Then, the analysis of extended failure modes, mechanisms and effects is utilized to construct fault evolution-test dependency. Using this integrated model, the designers and system analysts can assess the test suite＇s fault detectability, fault isolability and fault predictability. And heading attitude system application results show that the proposed model can support testability analysis and design for PHM very well.
基金funded by the “SMART BATTERY” project, granted by Villum Foundation in 2021 (project number 222860)。
文摘State of health(SoH) estimation plays a key role in smart battery health prognostic and management.However,poor generalization,lack of labeled data,and unused measurements during aging are still major challenges to accurate SoH estimation.Toward this end,this paper proposes a self-supervised learning framework to boost the performance of battery SoH estimation.Different from traditional data-driven methods which rely on a considerable training dataset obtained from numerous battery cells,the proposed method achieves accurate and robust estimations using limited labeled data.A filter-based data preprocessing technique,which enables the extraction of partial capacity-voltage curves under dynamic charging profiles,is applied at first.Unsupervised learning is then used to learn the aging characteristics from the unlabeled data through an auto-encoder-decoder.The learned network parameters are transferred to the downstream SoH estimation task and are fine-tuned with very few sparsely labeled data,which boosts the performance of the estimation framework.The proposed method has been validated under different battery chemistries,formats,operating conditions,and ambient.The estimation accuracy can be guaranteed by using only three labeled data from the initial 20% life cycles,with overall errors less than 1.14% and error distribution of all testing scenarios maintaining less than 4%,and robustness increases with aging.Comparisons with other pure supervised machine learning methods demonstrate the superiority of the proposed method.This simple and data-efficient estimation framework is promising in real-world applications under a variety of scenarios.
基金co-supported by the National Natural Science Foundation of China(Nos.U223321251875014)+1 种基金the Beijing Natural Science Foundation,China(No.L221008)the China Scholarship Council(No.202106020001).
文摘Current research on Digital Twin(DT)based Prognostics and Health Management(PHM)focuses on establishment of DT through integration of real-time data from various sources to facilitate comprehensive product monitoring and health management.However,there still exist gaps in the seamless integration of DT and PHM,as well as in the development of DT multi-field coupling modeling and its dynamic update mechanism.When the product experiences long-period degradation under load spectrum,it is challenging to describe the dynamic evolution of the health status and degradation progression accurately.In addition,DT update algorithms are difficult to be integrated simultaneously by current methods.This paper proposes an innovative dual loop DT based PHM framework,in which the first loop establishes the basic dynamic DT with multi-filed coupling,and the second loop implements the PHM and the abnormal detection to provide the interaction between the dual loops through updating mechanism.The proposed method pays attention to the internal state changes with degradation and interactive mapping with dynamic parameter updating.Furthermore,the Independence Principle for the abnormal detection is proposed to refine the theory of DT.Events at the first loop focus on accurate modeling of multi-field coupling,while the events at the second loop focus on real-time occurrence of anomalies and the product degradation trend.The interaction and collaboration between different loop models are also discussed.Finally,the Permanent Magnet Synchronous Motor(PMSM)is used to verify the proposed method.The results show that the modeling method proposed can accurately track the lifecycle performance changes of the entity and carry out remaining life prediction and health management effectively.
基金supported by the National Natural Science Foundation of China (51175502)
文摘Testing is the premise and foundation of realizing equipment health management （EHM）. To address the problem that the static periodic test strategy may cause deficient test or excessive test, a dynamic sequential test strategy （DSTS） for EHM is presented. Considering the situation that equipment health state is not completely observable in reality, a DSTS optimization method based on partially observable semi-Markov decision pro- cess （POSMDP） is proposed. Firstly, an equipment health state degradation model is constructed by Markov process, and the control limit maintenance policy is also introduced. Secondly, POSMDP is formulated in great detail. And then, POSMDP is converted to completely observable belief semi-Markov decision process （BSMDP） through belief state. The optimal equation and the corresponding optimal DSTS, which minimize the long-run ex- pected average cost per unit time, are obtained with BSMDP. The results of application in complex equipment show that the proposed DSTS is feasible and effective.
基金supported by the Fundamental Research Funds for The Central Universities(Grant No.2232021A-08)National Natural Science Foundation of China(GrantNo.51905091)Shanghai Sailing Program(Grand No.19YF1401500).
文摘The prognostics health management(PHM)fromthe systematic viewis critical to the healthy continuous operation of processmanufacturing systems(PMS),with different kinds of dynamic interference events.This paper proposes a three leveled digital twinmodel for the systematic PHMof PMSs.The unit-leveled digital twinmodel of each basic device unit of PMSs is constructed based on edge computing,which can provide real-time monitoring and analysis of the device status.The station-leveled digital twin models in the PMSs are designed to optimize and control the process parameters,which are deployed for the manufacturing execution on the fog server.The shop-leveled digital twin maintenancemodel is designed for production planning,which gives production instructions fromthe private industrial cloud server.To cope with the dynamic disturbances of a PMS,a big data-driven framework is proposed to control the three-level digital twin models,which contains indicator prediction,influence evaluation,and decisionmaking.Finally,a case study with a real chemical fiber system is introduced to illustrate the effectiveness of the digital twin model with edge-fog-cloud computing for the systematic PHM of PMSs.The result demonstrates that the three-leveled digital twin model for the systematic PHM in PMSs works well in the system’s respects.
基金the National Natural Science Founda-tion of China(Nos.91646205 and 71421002)the Fundamental Research Funds for the Central Universi-ties of China(No.16JCCS08)。
文摘The growing demand for health management puts forward high requirements for the quality of health knowledge.A content-based method is proposed to address the current demand for high-quality health knowledge,which evaluates the quality including the certainty,accuracy,and operability of different types of knowledge from the perspectives of authority,precision,and information entropy.Herein,the health knowledge of myocardial infarction is used as an example,and knowledge is first classified into different types and then evaluated.This method is applied to knowledge in the existing health management system and it can support knowledge screening and comparison under the cold start condition.Compared with the current evaluation methods based on knowledge use behavior and utility,the new evaluation method provides a reference for evaluation when the knowledge is first used.The screening of high quality knowledge can help the subsequent application of knowledge and improve user’s compliance.Concurrently,the arrangement of myocardial infarction knowledge can also provide a knowledge reference for patients’daily health management.
