Analysis Of Health Data Research Articles

Assessment of the integrated disease surveillance and response system implementation in health zones at risk for viral hemorrhagic fever outbreaks in North Kivu, Democratic Republic of the Congo, following a major Ebola outbreak, 2021

BackgroundThe Democratic Republic of the Congo (DRC) experienced its largest Ebola Virus Disease Outbreak in 2018–2020. As a result of the outbreak, significant funding and international support were provided to Eastern DRC to improve disease surveillance. The Integrated Disease Surveillance and Response (IDSR) strategy has been used in the DRC as a framework to strengthen public health surveillance, and full implementation could be critical as the DRC continues to face threats of various epidemic-prone diseases. In 2021, the DRC initiated an IDSR assessment in North Kivu province to assess the capabilities of the public health system to detect and respond to new public health threats.MethodsThe study utilized a mixed-methods design consisting of quantitative and qualitative methods. Quantitative assessment of the performance in IDSR core functions was conducted at multiple levels of the tiered health system through a standardized questionnaire and analysis of health data. Qualitative data were also collected through observations, focus groups and open-ended questions. Data were collected at the North Kivu provincial public health office, five health zones, 66 healthcare facilities, and from community health workers in 15 health areas.ResultsThirty-six percent of health facilities had no case definition documents and 53% had no blank case reporting forms, limiting identification and reporting. Data completeness and timeliness among health facilities were 53% and 75% overall but varied widely by health zone. While these indicators seemingly improved at the health zone level at 100% and 97% respectively, the health facility data feeding into the reporting structure were inconsistent. The use of electronic Integrated Disease Surveillance and Response is not widely implemented. Rapid response teams were generally available, but functionality was low with lack of guidance documents and long response times.ConclusionSupport is needed at the lower levels of the public health system and to address specific zones with low performance. Limitations in materials, resources for communication and transportation, and workforce training continue to be challenges. This assessment highlights the need to move from outbreak-focused support and funding to building systems that can improve the long-term functionality of the routine disease surveillance system.

Comparative Analysis of Dental Health Outcomes from Two Outreach Camps in Zanzibar, Tanzania: 2019 and 2023

Background: In low- and middle-income countries, oral health is frequently marginalized due to limited dental care access. This cross-sectional study assesses the outcomes of dental outreach camps in Zanzibar, Tanzania, organized in 2019 and 2023, to understand their impact on addressing local oral health needs over a four-year span. Methods: The study involved a thorough examination and analysis of clinical dental health data from both outreach camps. In 2019, 224 patients were examined, and in 2023, the number increased to 354. The assessment covered various dental health parameters, including dental caries, DMFT/dmft indices, enamel fluorosis, dental trauma, oral mucosa lesions, and non-carious tooth surface loss. Results: The analysis demonstrated a significant reduction in dental caries among adolescents and teens, decreasing from 62.5% in 2019 to 35.59% in 2023 (p < 0.0001). Similarly, the prevalence of teeth missing due to caries showed marked reductions from 40.2% to 9.04% in adolescents and teens (p < 0.0001) and from 25.89% to 11.86% among preschool and school-aged children (p = 0.004). Moreover, a significant change was observed in the Basic Erosive Wear Examination (BEWE) scores, with cases showing no erosive tooth wear (BEWE = 0) decreasing from 49.1% to 33.3% (p = 0.016), and cases with initial loss of enamel surface texture (BEWE = 1) increasing from 47.3% to 61% (p = 0.044). Conclusions: The outreach camps conducted in 2019 and 2023 showed tangible improvements in certain dental health metrics, particularly a reduction in dental caries and missing teeth due to caries among adolescents and teens. However, the uptick in non-carious tooth surface loss points to emerging dental health concerns. These results underline the importance of sustained and targeted dental health interventions to improve oral health outcomes in underserved communities such as Zanzibar.

Prediction of Stunting in Toddlers Combining the Naive Bayes Method and the C4.5 Algorithm

Research conducted to predict the incidence of stunting in toddlers, using data mining methods such as Naive Bayes and the C4.5 algorithm has been applied to analyze health data. The main aim of this research is to develop a predictive model that can identify toddlers who are at high risk of stunting, based on variables that have been collected from medical records and health surveys. The use of the Naive Bayes and C4.5 methods in this research aims to compare the effectiveness of the two methods in dealing with complex and unbalanced classification problems. This research involves a series of crucial stages starting from data selection, data pre-processing, data mining model design, data mining model testing, to method evaluation. In this study, the sample used consisted of 200 toddlers, of which 159 were diagnosed as having stunting and 41 others were not. The classification results show significant effectiveness in both methods used. The accuracy results of both methods are very encouraging, with both methods showing success rates of more than 90%. This shows that both Naive Bayes and C4.5 are very effective in identifying patterns related to the risk of stunting among toddlers. These highly accurate results not only demonstrate the power of data mining techniques in the field of public health but also provide insights that health practitioners can use to intervene earlier in at-risk populations.

Development of Machine Learning Model for Breast Cancer Prediction from Ultrasound Images

In the past decade, the revolution in information and computing technology has transformed approaches to breast cancer detection and treatment, with Machine Learning technologies offering significant potential in health data analysis. However, the development of accurate and reliable predictive models is faced with the challenges of data heterogeneity and complexity. This research proposes the development and validation of Machine Learning-based classification models using Support Vector Machine and Principal Component Analysis to address these issues, targeting improved accuracy in the early detection of breast cancer. The methodology applied involved the use of a breast cancer dataset from Kaggle, with data analysis conducted through inductive methods to identify relevant patterns. The combination of Support Vector Machine and Principal component Analysis achieved 89% accuracy in medical image classification, proving its efficacy in breast cancer diagnostics and providing a more reliable model for early detection. The implications of these findings are significant, both theoretically and practically, for the fields of Machine Learning and Breast Cancer, expanding the understanding of the applications of advanced data processing techniques. Although this study faces limitations in the variability of the dataset's patient characteristics, the results offer a basis for further development in diagnostic technology while recommending the integration of Deep Learning and Big Data analysis as a direction for future research.

Transformative Applications of IoT in Diverse Industries: A Mini Review

By integrating physical objects and facilitating data-driven decision-making, the Internet of Things (IoT) is transforming several sectors. Through the provision of individualised treatment plans, real-time health data analysis, and remote patient monitoring, it is vital to the modernization of healthcare systems. IoT technologies are essential to the development of smart cities, resource allocation optimisation, public safety improvement, and traffic congestion reduction. IoT-driven smart farming automates machinery, optimises irrigation, and monitors crop conditions. As IoT makes it possible to create smart grids, save energy waste, and increase grid dependability, the energy landscape is changing. IoT makes it easier to apply Industry 4.0 ideas in the manufacturing sector, converting conventional factories into networked, intelligent systems. Reducing operating costs and increasing productivity are the outcomes of implementing IoT-enabled sensors, robots, and data analytics to improve supply chain management, predictive maintenance, and production efficiency. Innovation, sustainability, and efficiency are becoming more and more possible as a result of the Internet of Things' integration across many industries. This review also showcases the relevant prospects of IoT applications in the fields mentioned.

Applying Advanced Practice Nurses in Rural Japan: A Mixed-Methods Perspective.

Introduction Rural hospitals, particularly those in geographically isolated regions like Shimane Prefecture, Japan, face significant healthcare delivery challenges. These include limited resources, an aging population, and a scarcity of healthcare professionals. Advanced practice nurses (APNs) have emerged as pivotal in addressing these gaps, offering specialized patient assessment, diagnosis, and management skills. This study aimed to evaluate the demand for APNs in rural community hospitals, focusing on the specific educational needs and clinical competencies required to improve healthcare outcomes in these settings. Method Employing a mixed-methods approach, this research combined qualitative insights from stakeholder interviews with quantitative data analysis of electronic health records (EHRs) at Unnan City Hospital. This sequential exploratory design aimed to capture comprehensive educational needs and outcomes, integrating the depth of qualitative data with the breadth of quantitative evidence to tailor a curriculum for APNs in rural healthcare contexts. Results The study revealed a critical demand for APNs skilled in managing common medical issues in rural settings, such as infections, circulatory failures, and respiratory problems. Stakeholder interviews highlighted the necessity for a curriculum that enhances clinical competencies and emphasizes soft skills like communication and leadership. An analysis of EHRs identified 21 specific diseases across six categories, underlining the importance of targeted education on these prevalent conditions. Conclusion The findings underscore the urgent need for specialized education programs for APNs in rural hospitals to address these communities' unique healthcare challenges. Developing a curriculum that focuses on clinical and soft skills essential for rural healthcare delivery can significantly enhance the quality of care. This study advocates for implementing such tailored educational programs to empower APNs, thereby contributing to healthcare equity and improving patient outcomes in rural settings.

Supporting re-engagement with HIV services after treatment interruption in South Africa: a mixed method program evaluation of MSF’s Welcome Service

Psychosocial challenges impact patients’ ability to remain on antiretroviral therapy lifelong, magnified by disorganized health-systems and healthcare worker (HCW) attitudes. To address this, Médecins Sans Frontières and the Department of Health developed the Welcome Service intervention, to provide person-centered care at re-engagement after HIV treatment interruption. Implemented in Khayelitsha, South Africa, between August 2020 and February 2021, the intervention aimed to reorganize triage, optimize clinical and counselling services and address HCW attitudes. The study used a mixed-methods design, incorporating in-depth interviews, and analyses of programmatic and routine health data. Interviews demonstrated positive patient care experiences. HCWs understood the potential impact of attitudes on patient engagement, however, some continued to demonstrate judgmental attitude. Clinical objectives were variably met at re-engagement: 98% were re-initiated the same day, 50% had a CD4 done, and 45% received tuberculosis prevention. Nevertheless, 4-month retention was 66%, and 88% had a VL < 1000 c/mL. Despite HCWs’ understanding of person-centered care not translating into supportive behaviors, patients had positive care experiences and the intervention ended with a high rate of VL suppression. More efforts are needed to design interventions building on Welcome Service principles to provide person-centered care and sustain retention after re-engagement.

Computer-Aided Design in Sports Nutrition and Health Data Analysis of College Students Using Artificial Intelligence Data Mining

Computer-Aided Design in Sports Nutrition and Health Data Analysis of College Students Using Artificial Intelligence Data Mining

Abstract 2160: Utilizing geospatial analytics to identify neighborhood cancer hot spots for recruitment of underserved populations in clinical prevention research

Abstract Background: The term “precision public health” has increasingly been used in literature to describe public health practices that utilize new technologies and big data to precisely target the most vulnerable populations for disease control. Following the principles of precision public health, we used geospatial technologies and population-based health data to identify the locations and socioeconomic status of neighborhoods with high cancer incidences for recruitment of participants in prevention research. Methods: Using Florida cancer registry data, geospatial hot spot analyses were carried out to identify statistically significant clusters of census tracts with high incidences of prostate, gastric, and oral cancers within the catchment area of the Sylvester Comprehensive Cancer Center in South Florida. The locations, social determinants of health (US Census’ American Community Survey), and behavioral risk factor data (CDC’s Behavioral Risk Factor Surveillance System) of these hot spots were analyzed to identify the locations of underserved communities facing high risk for these three cancers. Results: Hot spots with high incidences for each of these three cancers overlap in many neighborhoods with high proportions of Hispanic and Non-Hispanic Black populations and high levels of socioeconomic disparities, such as low income, low education level, rental housing, no vehicles, and no access to health insurance. These neighborhoods also have high rates for behavioral risk factors in smoking, lack of physical activity, obesity, and high incidence of cervical cancer (high risk areas for HPV-associated oral cancers). Mobile screening units called the Game Changer Vehicles have been deployed to the locations to recruit participants who were tested on-site for prostate specific antigen (prostate cancer), H. pylori (gastric cancer), or solCD44 (a soluble tumor marker for squamous cell carcinoma) and total protein levels in oral rinse specimens, which can distinguish individuals with molecular features at risk for oral cancers due to tobacco, alcohol use, and human HPV infection. Conclusions: Geospatial analyses of population health data can be used to produce metrics that precisely target neighborhoods with high cancer risk and health disparities to recruit research participants. The same precision approach can also be applied for cancer screening purposes, such as offering cervical self-sample and fecal blood test kits in disadvantaged communities facing heightened risk for cervical and colorectal cancers. Citation Format: Ming S. Lee, Rebecca Kaiser, Ridhi Vyas, Amando Vera, Daniela Flores Quetant, Nancy Elliott, Valerie Bethel, Amanda Rivera, Shria Kumar, Brandon Mahal, Elizabeth Franzmann, Erin Kobetz. Utilizing geospatial analytics to identify neighborhood cancer hot spots for recruitment of underserved populations in clinical prevention research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2160.

Human epididymis protein 4: Analysis of national health and nutrition examination survey data

BackgroundHuman epididymis protein 4 (HE4) is a tumor marker overexpressed in ovarian cancer and is commonly utilized to aid with diagnosis of an adnexal mass. HE4 levels vary based on pregnancy, age, menopausal status, and tobacco use. Objective(s)The objective of this study was to evaluate population-based data to examine factors that affect HE4 among adult women in the United States and stratify levels of HE4 by demographic and gynecologic factors. Study designA retrospective analysis was conducted using data from 2,480 women aged 20 + who participated in the National Health and Nutrition Examination Survey (2001–2002). From these cross-sectional data, serum HE4 and cotinine, a marker of tobacco exposure, were combined with demographic and interview data. Estimated glomerular filtration rates (eGFR) were based on serum creatinine, age, sex, and race. Other variables of interest included menopausal status, pregnancy, and various gynecologic factors. Summary HE4 data are provided as geometric means with associated 95 % confidence intervals. ResultsHE4 levels were independently associated with age, renal function, and nicotine use, all p < 0.001. Pre-menopausal women with a history of endometriosis were found to have elevated HE4 levels compared to those without, p < 0.01; however, we found no such difference among post-menopausal women. Adjusting for age, no differences in HE4 were found based on race/ethnicity, p = 0.29. HE4 levels showed statistically significant associations with income level; however, these were small and clinically irrelevant. ConclusionThis study provides evaluation of HE4 levels among a data set representative of 98.5 million non-institutionalized women in the United States and gives insight into extraneous factors that may influence these levels.

Investigating the spatiotemporal associations between meteorological conditions and air pollution in the federal state Baden-Württemberg (Germany)

When analyzing health data in relation to environmental stressors, it is crucial to identify which variables to include in the statistical model to exclude dependencies among the variables. Four meteorological parameters: temperature, ultraviolet radiation, precipitation, and vapor pressure and four outdoor air pollution parameters: ozone (O3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext{O}_3$$\\end{document}), nitrogen dioxide (NO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext{NO}_2$$\\end{document}), particulate matter (PM2.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PM_{2.5}$$\\end{document}, PM10\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PM_{10}$$\\end{document}) were studied on a daily basis for Baden-Württemberg (Germany). This federal state covers urban and rural compartments including mountainous and river areas. A temporal and spatial analysis of the internal relationships was performed among the variables using (a) cross-correlations, both on the grand ensemble of data as well as within subsets, and (b) the Local Indications of Spatial Association (LISA) method. Meteorological and air pollution variables were strongly correlated within and among themselves in time and space. We found a strong interaction between nitrogen dioxide and ozone, with correlation coefficients varying over time. The coefficients ranged from negative correlations in January (−0.84), April (−0.47), and October (−0.54) to a positive correlation in July (0.45). The cross-correlation plot showed a noticeable change in the correlation direction for O3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext{O}_3$$\\end{document} and NO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext{NO}_2$$\\end{document}. Spatially, NO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext{NO}_2$$\\end{document}, PM2.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PM_{2.5}$$\\end{document}, and PM10\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PM_{10}$$\\end{document} concentrations were significantly higher in urban than rural regions. For O3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext{O}_3$$\\end{document}, this effect was reversed. A LISA analysis confirmed distinct hot and cold spots of environmental stressors. This work examined and quantified the spatio-temporal relationship between air pollution and meteorological conditions and recommended which variables to prioritize for future health impact analyses. The results found are in line with the underlying physico-chemical atmospheric processes. It also identified postal code areas with dominant environmental stressors for further studies.

Analysis of Health Data for the Elderly based on Medical Website Mining

The development of Internet medicine provides a convenient platform for doctor-patient communication, and provides an important data source for paying attention to the health of the elderly. In this study, a large number of consultation records obtained from the medical website was screened and cleaned, and the medical thesaurus was generated by training of its own data to solve the problem of inaccurate professional terminology segmentation caused by using default jieba segmentation. At the same time, we use the trained medical thesaurus to conduct topic mining of the consultation data, and it is found that the most concerned problems in the field of elderly health are cerebro-cardiovascular, pulmonary and stomach diseases, so as to provide further medical advice and targeted services.

QQAR: A Q-learning-based QoS-aware routing for IoMT-enabled wireless body area networks for smart healthcare

Recently, the Internet of Medical Things (IoMT) has gained broad acclaim in the academic and industrial sectors due to its use in integrating medical devices and healthcare systems. Wireless body area networks (WBANs) are a promising technology in smart healthcare for real-time patient monitoring and health data analysis by medical professionals. Efficient data transmission is essential in ensuring reliable and timely delivery of healthcare facilities. Healthcare services require reliable, delay-sensitive, energy- and congestion-aware communication, which makes it challenging to design a routing protocol. To address quality of services (QoS) in IoMT-enabled WBANs for healthcare, we propose Q-learning-based quality of services (QoS) aware routing (QQAR). Firstly, we used two-hop-based link reliability estimation to ensure link reliability from sources to sink nodes. Routing decisions using two-hop neighbor information guarantees successful packet delivery. Secondly, we differentiated the packets based on traffic priority, which assures the delivery of critical packets even in a congested network. Particularly, considering two-hop node velocity with energy balancing enhanced the guarantee of timely packet delivery before deadlines. In addition, the multi-sink approach enhanced network reliability. Finally, we utilized Q-learning to select suitable neighbor nodes for packet forwarding in a decentralized fashion. A multi-sink load balance and congestion control mechanism adjusts the routing decisions in the QQAR to avoid congestion. Our simulation and comparison results showed that QQAR outperforms the existing routing protocols across various performance metrics under distinct network conditions.

Patient and Public Willingness to Share Personal Health Data for Third-Party or Secondary Uses: Systematic Review.

International advances in information communication, eHealth, and other digital health technologies have led to significant expansions in the collection and analysis of personal health data. However, following a series of high-profile data sharing scandals and the emergence of COVID-19, critical exploration of public willingness to share personal health data remains limited, particularly for third-party or secondary uses. This systematic review aims to explore factors that affect public willingness to share personal health data for third-party or secondary uses. A systematic search of 6 databases (MEDLINE, Embase, PsycINFO, CINAHL, Scopus, and SocINDEX) was conducted with review findings analyzed using inductive-thematic analysis and synthesized using a narrative approach. Of the 13,949 papers identified, 135 were included. Factors most commonly identified as a barrier to data sharing from a public perspective included data privacy, security, and management concerns. Other factors found to influence willingness to share personal health data included the type of data being collected (ie, perceived sensitivity); the type of user requesting their data to be shared, including their perceived motivation, profit prioritization, and ability to directly impact patient care; trust in the data user, as well as in associated processes, often established through individual choice and control over what data are shared with whom, when, and for how long, supported by appropriate models of dynamic consent; the presence of a feedback loop; and clearly articulated benefits or issue relevance including valued incentivization and compensation at both an individual and collective or societal level. There is general, yet conditional public support for sharing personal health data for third-party or secondary use. Clarity, transparency, and individual control over who has access to what data, when, and for how long are widely regarded as essential prerequisites for public data sharing support. Individual levels of control and choice need to operate within the auspices of assured data privacy and security processes, underpinned by dynamic and responsive models of consent that prioritize individual or collective benefits over and above commercial gain. Failure to understand, design, and refine data sharing approaches in response to changeable patient preferences will only jeopardize the tangible benefits of data sharing practices being fully realized.

Estimating Risk at the Energy Production Unit of “AHS” Keratea-Lavrio

Every business should protect its employees in matters of Safety and Health. It is deemed necessary to access, analyze and classify risks to achieve these issues. The study aims to analyse and estimate occupational risk based on the appropriate empirical " Checklist " method by the responsible staff. Specifically, the relevant legislation and Regulations-Directives from Greek and European sources were studied to approach the Safety and Health issues and data for Risk Analysis. Considering the theoretical background of the energy production, a rapid visual screening of the Unit of Keratea-Lavrio Steam Power Station (“AHS” Keratea-Lavrio) was conducted. In addition, discussions were held with the facilities' managers, who are responsible for developing and implementing safety and health plans and highlighting and analyzing critical points of the station. Afterwards, the total risk(after taking into account several factors) is identified and classifiedinto three categories, revealing the corresponding risk reduction measures. Conclusions about safety and hygiene were drawn about the “AHS” Keratea-Lavrio Energy Production Unit.

Analysis of youth sports physical health data based on cloud computing and gait awareness

Abstract Sub-health problems are becoming increasingly serious in today’s society, and some organizations are not paying enough attention to adolescent sports health data. For adolescent sports, health needs to be measured regularly and tested constantly so that the intake of diet and medication can be reasonably adjusted according to their biochemical indicators. The Smart Health Life Growth Cloud System can effectively manage residents’ health data digitally and informally, enabling users to manage their health data better and facilitating doctors to keep abreast of users’ health conditions, while also facilitating the government to conduct research and studies on the physical fitness of adolescents in the areas under its jurisdiction. The cloud-based management platform for student physical health management relies on the mobile internet as a practical service platform whose primary role is to provide young people with a convenient sporting life, focusing on practicality, service, and interactivity. We also collect sensor data to detect gait patterns (with or without leg contact) and filter them through an adaptive hybrid filter to differentiate between the two patterns. In turn, the Smart Health Life Growth Cloud system changes the traditional medical model and greatly improves the information and intelligence of the healthcare industry. Using the exercise individual health evaluation model in this article is controlled to be within 20%, thus concluding that the exercise individual health evaluation model proposed in this article can predict the exercise limit of an exercise individual more accurately.

Optuna Based Hyperparameter Tuning for Improving the Performance Prediction Mortality and Hospital Length of Stay for Stroke Patients

Cardiovascular disease (CVD) stands as the foremost contributor to worldwide mortality, with strokes as part of significant CVD. Research on potential mortality risks and hospitalizations for stroke patients became crucial as a basis for evaluation to improve the quality and control of stroke patient services. Although machine learning technology has been widely used in health data analysis, understanding the relative performance and characteristics of machine learning (ML) models is still limited. Therefore, the study aims to broaden this understanding by comparing five ML models, namely XGBoost, Random Forest, Decision Trees, CatBoost, and Extra Trees, using stroke patient data from RSUD Banyumas Neural Poliklinik Indonesia. The model performance improvement process is the main focus, involving adjustments using the Optuna tuning library. Through this tuning approach, the key parameters of each ML model are optimally adjusted to improve their performance in predicting mortality risk and the duration of hospitalization for stroke patients. As a result, the XGBoost algorithm proved superior in predicting mortality (accuracy 86%, AUC 0.87) and the duration of hospitalization (accuracy 82%, AUC 0.79). This research has great potential to help hospitals identify high-risk stroke patients and plan more efficient treatment. This approach allows hospitals to use their resources better, improve medical services, and reduce unnecessary treatment costs

Understanding spatiotemporal patterns of COVID-19 incidence in Portugal: A functional data analysis from August 2020 to March 2022.

During the SARS-CoV-2 pandemic, governments and public health authorities collected massive amounts of data on daily confirmed positive cases and incidence rates. These data sets provide relevant information to develop a scientific understanding of the pandemic's spatiotemporal dynamics. At the same time, there is a lack of comprehensive approaches to describe and classify patterns underlying the dynamics of COVID-19 incidence across regions over time. This seriously constrains the potential benefits for public health authorities to understand spatiotemporal patterns of disease incidence that would allow for better risk communication strategies and improved assessment of mitigation policies efficacy. Within this context, we propose an exploratory statistical tool that combines functional data analysis with unsupervised learning algorithms to extract meaningful information about the main spatiotemporal patterns underlying COVID-19 incidence on mainland Portugal. We focus on the timeframe spanning from August 2020 to March 2022, considering data at the municipality level. First, we describe the temporal evolution of confirmed daily COVID-19 cases by municipality as a function of time, and outline the main temporal patterns of variability using a functional principal component analysis. Then, municipalities are classified according to their spatiotemporal similarities through hierarchical clustering adapted to spatially correlated functional data. Our findings reveal disparities in disease dynamics between northern and coastal municipalities versus those in the southern and hinterland. We also distinguish effects occurring during the 2020-2021 period from those in the 2021-2022 autumn-winter seasons. The results provide proof-of-concept that the proposed approach can be used to detect the main spatiotemporal patterns of disease incidence. The novel approach expands and enhances existing exploratory tools for spatiotemporal analysis of public health data.

Technological innovations in mitigating winter health challenges in New York City, USA

This review paper examines the role of technological innovations in mitigating winter health challenges in New York City (NYC). The primary objective is to analyze how emerging technologies are integrated into public health strategies to address the unique health risks posed by the winter season in one of the world's most populous urban environments. The methodology involves a comprehensive literature review focusing on recent advancements in technology within the context of public health and urban planning. Key sources include peer-reviewed articles, case studies, and reports on smart city initiatives, climate change impacts, and the implementation of health technologies in NYC. Key findings reveal that the integration of smart city technologies, such as advanced data analytics, IoT devices, and AI-driven solutions, has significantly improved healthcare delivery and emergency response during winter. However, challenges in implementation, particularly in under-resourced settings, highlight the need for tailored, context-driven technological solutions. The impact of climate change on winter health dynamics underscores the importance of innovative urban planning and public health strategies. The potential of emerging technologies, including nanotechnology for air purification and spatial analysis of health data, offers promising avenues for improving public health in urban settings during winter. The review suggests that technological innovations play a crucial role in enhancing the capacity of NYC to manage winter health risks. It recommends increased investment in smart city technologies, support for under-resourced healthcare settings, and the development of climate change adaptation strategies. The evolution of healthcare in winter contexts is increasingly being shaped by these technological advancements, paving the way for a more resilient and sustainable approach to public health in urban environments.

Functional data analysis in health informatics: A comparative review of developments and applications in the USA and Africa

This study presents a comprehensive review of Functional Data Analysis (FDA) in Health Informatics, focusing on a comparative analysis between the United States and Africa. The background of the study is rooted in the growing importance of FDA in the healthcare sector, driven by technological advancements and the increasing complexity of health data. The aim of the paper is to explore and contrast the developments, applications and challenges of FDA in health informatics within these two diverse geographical contexts. The scope of the paper encompasses the historical evolution, theoretical frameworks, technological advancements, structural differences in health data management and ethical considerations in both the USA and Africa. Methodologically, the study employs a qualitative analysis framework, integrating a systematic review of relevant literature. This approach facilitates an in-depth examination of the divergent paths and shared challenges in FDA across the two regions. The main findings reveal significant disparities in the evolution, application and effectiveness of FDA in health informatics. The USA demonstrates a leading role with advanced technological integration and innovative applications, while Africa shows resilience and adaptability despite facing challenges of limited resources and infrastructure. The study concludes that while the USA has made significant strides in utilizing data to enhance public health and healthcare delivery, Africa presents a landscape of both challenges and opportunities. The divergence is influenced by disparities in educational infrastructure, access to technological resources and healthcare delivery systems. Recommendations include the need for global collaboration, investment in health informatics education, workforce development and the establishment of global standards for health data analysis and sharing.