Search Results (408)

In the era of big data, the demand for vast quantities of diverse and representative datasets has surged across various domains, from healthcare and finance to artificial intelligence and machine learning. Synthetic data generation offers a promising solution by enabling the creation of data with specific properties that closely mimic real-world data while avoiding privacy concerns and regulatory limitations. However, generating high-quality synthetic data that accurately preserves complex dependencies remains a significant challenge. This paper addresses this gap by exploring a novel approach: Inverse Copula Sampling for Multi-Dimensional Data Synthesis. Utilizing copulas, which are powerful tools for modeling dependencies between variables, our method generates synthetic data that maintains intricate interdependencies. We demonstrate the effectiveness of this approach through various experiments and case studies, showing high fidelity in preserving dependencies and minor discrepancies in marginal distributions. The method’s robustness was validated through comparative analysis and statistical checks, including the Kolmogorov–Smirnov test. Our research contributes to the field by introducing a flexible and efficient method for synthetic data generation that is applicable to a wide range of data distributions and practical applications. Future work will explore the application of other copula types and the further refinement of the method to enhance its versatility. Full article

Background and Objectives: HPV vaccination is critical for cervical cancer prevention, but HPV vaccination coverage stays low among young women in China. While various factors shaping vaccination willingness have been explored, the role of personality traits remains insufficiently understood. This study investigated the relationships between Big Five personality traits and three facets of HPV vaccination willingness (consideration, determination, recommendation) among female healthcare undergraduates. We also specifically examined whether academic major moderated the association between extraversion and vaccination willingness. Methods: A cross-sectional survey was conducted among 703 female undergraduates enrolled in healthcare majors at a medical college in Chengdu using a stratified cluster sampling method. Data were collected via online questionnaires, including the 10-item Big Five Inventory (BFI-10) and a scale measuring three motivational facets of HPV vaccination willingness: consideration, determination and recommendation. Descriptive statistics, correlation analysis and hierarchical regression analysis were performed to examine the research questions. Results: A total of 672 valid questionnaires were analyzed. Distinct associations were found between personality dimensions and each facet of vaccination willingness. Agreeableness (β = 0.193, p < 0.01) and openness (β = 0.079, p < 0.05) correlated with vaccination consideration (ΔR² = 0.061). Extraversion (β = 0.103, p < 0.05), agreeableness (β = 0.113, p < 0.01) and conscientiousness (β = 0.096, p < 0.05) correlated with determination (ΔR² = 0.049). Extraversion (β = 0.125, p < 0.01), agreeableness (β = 0.129, p < 0.01), conscientiousness (β = 0.102, p < 0.01) and neuroticism (β = 0.081, p < 0.05) were positively related to recommendation willingness (ΔR² = 0.061). Academic major did not exert a significant moderating effect. Conclusions: The findings highlight the complexity of the relationships between personality traits and vaccination attitudes and behaviors. While the observed associations were modest in magnitude, this study provides preliminary empirical evidence that may inform personality-sensitive HPV vaccination promotion strategies. Tailored, multi-dimensional interventions that consider diverse population characteristics could be considered to optimize HPV vaccination outreach, though their effectiveness awaits further validation. Full article

The quick transition of the healthcare industry to digital during the era of the Internet of Medical Things and Artificial Intelligence has ignited the demand for frameworks for data sharing while retaining safety and patient privacy. Centralized learning models place potentially sensitive patient data at risk of leakage, regulatory violation, and cyber-attacks which undermine receptivity and responsible ownership of big medical data. Federated learning is a novel paradigm that allows patients from various healthcare entities to train machine learning models while maintaining the ability to leverage their data without sharing their direct data. This study proposes a systematic literature review of approaches of privacy-preserving federated learning frameworks in healthcare applications. Following PRISMA guidelines, searches were conducted across Web of Science, Scopus, IEEE Xplore, ScienceDirect, PubMed, and ACM Digital Library with predefined query strings, explicit inclusion/exclusion criteria, and quality appraisal procedures. A total of 80 peer-reviewed studies, published from January 2015 to December 2025, were included in this systematic review, which examined cryptographic, architectural and algorithmic methods including differential privacy, homomorphic encryption, and Secure Multi-Party Computation, along with integrations using blockchain to enhance trust and confidence in distributed healthcare systems. The findings indicate a gradual shift towards hybrid privacy-preserving federated learning architectures which combined multiple security mechanisms to improve trust, confidentiality and robustness. Although significant progress has been achieved, the real-world deployment of such systems is heavily affected due to the challenges in communication efficiency, non-IID data distribution, adversarial attacks, and regulatory requirements. This research highlights future research directions for scalable, explainable and interoperable federated architectures that strike an optimal balance of privacy, utility and system performance for next-gen health intelligence. Trial registration: PROSPERO (CRD420261401073). Full article

Practical socioeconomic systems commonly contain imprecise and subjective data, while existing uncertain regression methods perform poorly for highly multicollinear variables. Uncertain least squares is susceptible to multicollinearity and outliers, and uncertain LASSO fails to stably select correlated variables. To address these issues, this paper proposes an uncertain elastic net regression model targeting multicollinear uncertain data. Based on the minimum uncertain expectation framework, the model adopts combined regularization to realize sparse variable screening and grouping effect, which mitigates multicollinearity and enhances estimation stability. We verify the model via numerical examples and an empirical study on Shandong’s domestic tourism data, taking two classic uncertain regression methods as benchmarks. The results show that our model outperforms competitors in fitting accuracy, coefficient stability and variable selection. This method provides a reliable, interpretable tool for regression modeling under uncertainty and multicollinearity, and can be applied to tourism and socioeconomic research. Full article

Big-data analytics are increasingly used in scouting and talent identification, with machine learning (ML) tools applied to evaluate and predict player performance based on match statistics, video tracking, physical and anthropometric tests, psychological assessments, social media data, and qualitative scouting reports. Advances in computer vision, together with the emergence of affordable automated broadcasting and data collection systems, have extended the deployment of ML-driven scouting from professional to youth sport. The use of algorithms in educational, employment, and healthcare settings has been shown to introduce biases and discrimination while wrongly assuming accuracy and objectivity because the decisions are made automatically and quantitatively. In this respect, we briefly describe the development of data-driven performance analysis and how ML-based technologies are currently applied for early screening and comparison of large player populations. Based on a narrative overview of the literature, we draw on evidence from education, employment, and healthcare to identify risks that may also emerge in ML-driven player evaluation, including algorithmic bias, non-representative training data, privacy concerns, and the persistence of model-based labels over time, especially in youth sport. Our main contribution is translating these threats into governance principles and operational safeguards for responsible use of AI in scouting and talent identification. Full article

Background/Objectives: Epilepsy is a long-term condition that affects the brain and has a big impact on a person’s daily life, especially in areas where people do not have a lot of money or access to good healthcare. This study aimed to evaluate the relationship between medication adherence and QoL and to assess the role of seizure severity in the association between them among patients with epilepsy. Methods: A cross-sectional study of 1100 adult patients with epilepsy was conducted using registry data and structured interviews. The main outcomes that were assessed are quality of life (QoL), medication adherence, and seizure severity. Results: Reduced QoL was observed in 62% of patients. Low medication adherence was significantly associated with reduced QoL (OR = 4.33 [3.24–5.79] unadjusted; 3.90 [3.07–5.80] fully adjusted). Seizure severity was also associated with reduced QoL (OR = 1.62, p = 0.002; OR = 2.05, p < 0.001). Cognitive impairment showed the strongest association with reduced QoL, with ORs of 14.6 for mild and 80.8 for moderate-severe impairment in unadjusted models, remaining significant after adjustment. Medication adherence was significantly associated with seizure severity (OR = 1.18, p = 0.002), and attenuation of its effect after adjustment suggests that these variables are interrelated, although causality cannot be determined in this study. Additional factors associated with reduced QoL included lower education, longer disease duration, polytherapy, structural brain abnormalities, and comorbidities. Conclusions: Reduced QoL in epilepsy is strongly influenced by cognitive impairment and medication nonadherence, with seizure severity potentially contributing to this association, although causality cannot be inferred. These findings support integrated care strategies targeting adherence, cognition, and seizure control to improve patient outcomes. Full article

Background/Objectives: Patient-Reported Experience Measures (PREMs) help us understand how patients perceive healthcare quality. Yet most studies look at complaints in isolation, without tying them to the structural features of medical practice. This study asks whether the nature of clinical work—shaped by diagnostic pathways, interaction patterns, and professional focus—predicts what patients complain about. Methods: We analyzed 18,492 negative reviews from infodoctor.ru, collected between 2012 and 2023 across 16 Russian cities with populations over one million. We used a mix of methods: machine learning (logistic regression) to classify complaints as medical (M-type) or organizational (O-type), statistical tests (chi-square, proportion analysis), and expert validation by nine independent specialists. We also built a novel multidimensional classification of medical practices based on three criteria: diagnostic pathway length, frequency and duration of patient interaction, and whether the work is mainly technical or communicative. Results: Technical specialties received far more medical complaints than communicative ones (39.8% vs. 29.3%, p < 0.001), while communicative specialties received more organizational complaints (45.7% vs. 35.0%, p < 0.001). Specialties that manage chronic conditions over the long term had the highest share of organizational complaints (41.6%). At the city level, the share of communicative specialists correlated negatively with complaints per capita (r = −0.541, p = 0.0306). We found no meaningful gender differences in complaint patterns. Conclusions: The type of medical practice systematically shapes what patients complain about. Technical specialties draw criticism on clinical quality; communicative specialties draw criticism on how care is organized. Long-term care faces challenges rooted more in administrative friction than in clinical competence. These findings show that PREMs, when analyzed through a practice-based lens, can support targeted quality improvement—moving from simply tracking complaints to acting on them in specialty-specific ways. Full article

Patients leave millions of medical reviews annually, providing critical data for quality management. However, manual processing is infeasible, and existing systems fail to distinguish medical from organizational problems—a distinction essential for complaint routing. The consequences of misrouting are significant: clinical issues may go unaddressed when medical complaints reach administrative staff, while systemic service problems remain unresolved when organizational complaints reach medical directors. We developed a hybrid approach combining expert annotation with Large Language Models (LLMs). Fifteen prompt iterations on 1500 reviews with expert validation (modified Cohen’s kappa (κ_mod), which weights errors hierarchically, reached 0.745) preceded the LLM annotation of 15,000 mixed-sentiment and positive reviews. These were combined with 7417 expert-annotated negative reviews to form a corpus of 22,417 reviews. Eight architectures, ranging from Logistic Regression to a BERT + TF-IDF + LightGBM ensemble, were compared using both standard metrics and domain-specific practical metrics tailored to complaint routing. The best model, scaled to 4.3 million Russian-language reviews from the Prodoctorov.ru platform, achieved 92.9% Practical Accuracy—the proportion of reviews classified without critical medical–organizational misclassification errors (M ↔ O)—compared to 68.0% standard accuracy, which treats all errors equally. Critical errors were reduced to 1.4%, yielding 144,000 more correctly processed complaints than traditional methods (TF-IDF + Logistic Regression). Analysis of the scaled data revealed the following: 46.1% M (medical), 21.0% O (organizational), and 32.9% C (combined) reviews; medical ratings were highest (4.75 vs. 4.59 for organizational, p < 0.001); combined reviews were longest (802 characters); zero-star reviews comprised 3.8% of feedback, with organizational complaints dominating (38.2%) among extreme negatives; and average ratings rose by 1.24 points over 14 years. This hybrid approach yields expert-comparable corpora, automates 93% of feedback processing, ensures correct complaint routing, and contributes to healthcare sustainability by reducing administrative burden, accelerating resolution, and enabling data-driven quality management without proportional increases in human resources. All analyses were conducted on Russian-language patient reviews. Full article

Background/Objectives: Abnormal muscle tone and impaired motor control commonly limit gait recovery after stroke. Robot-assisted gait training has been introduced to augment conventional rehabilitation; however, its effects on stage-based motor recovery, functional ambulation, and muscle tone during the subacute phase remain unclear. Methods: This prospective, single-center, randomized controlled trial enrolled 30 patients with subacute stroke who received robot-assisted gait training plus conventional rehabilitation (R-BoT Plus group, n = 15) or conventional rehabilitation alone (control group, n = 15) over 4 weeks. The primary outcome was the change in Brunnstrom recovery stage of the lower extremities (BRS-LE). Secondary outcomes included Functional Ambulation Category (FAC), Fugl–Meyer Assessment for the Lower Extremity (FMA-LE), clinical spasticity measures (Modified Ashworth Scale and Modified Tardieu Scale), and muscle mechanical properties (MyotonPRO). Exploratory analyses were conducted to examine the associations between changes in stage-based motor recovery (ΔBRS-LE), functional ambulation (ΔFAC), and MyotonPRO parameters. Within-group changes were assessed using the Wilcoxon signed-rank test. Between-group effects were primarily evaluated using baseline-adjusted ANCOVA with HC3 robust standard errors, with Wilcoxon rank-sum tests on change scores as sensitivity analyses. Associations between changes in clinical outcomes and MyotonPRO parameters were evaluated using Spearman’s rank correlation coefficient (ρ). Results: BRS-LE (p = 0.014) and functional ambulation (p = 0.041) were significantly improved in the R-BoT Plus group. Changes in FMA-LE and clinical spasticity measures did not differ significantly between groups. Quantitative myotonometry revealed selective muscle- and parameter-specific changes. No robust correlations were observed between MyotonPRO parameters and changes in BRS-LE. Conclusions: The addition of robot-assisted gait training to conventional rehabilitation was associated with greater improvements in stage-based lower-limb motor recovery and functional ambulation in patients with subacute stroke. In contrast, cumulative impairment scores and conventional clinical spasticity measures demonstrated limited changes between groups. Quantitative muscle mechanical assessment revealed selective muscle-specific adaptations, supporting its role as a complementary tool for mechanistic characterization rather than as a surrogate marker of motor recovery. Future studies incorporating dose-matched designs and longer follow-up periods are warranted to clarify the independent and long-term effects of robot-assisted gait training. Full article

Due to the significant challenges faced by healthcare systems, medical establishments strive to set the tone by integrating new concepts to bridge this gap. Here, Lean Healthcare (LH) has been inspired by Lean Management (LM). Utilizing LM to optimize industrial processes and reduce waste presented a real opportunity to enhance the quality of medical services. For more improvement, healthcare systems pushed themselves to keep up with progress by implementing Industry 4.0 (I4.0) tools, such as IoT, Big Data analytics, and AI with LH and sustainability practices. The results promised better quality of care. Although this concept offers significant potential for more efficient workflows and optimizing medical processes, studies examining their combined implementation are still scarce. This research fills the gap via a literature review (LR) of peer-reviewed articles published between 2015 and 2025. The review investigates the impact of integrating smart technologies into LH frameworks and highlights how LH contributes to sustainability across multiple dimensions: economic, social, technological and environmental. Key findings show the impact of combining advanced tools with lean principles by reducing waiting times (25%) and length of stay while also improving satisfaction. Sustainability-centered adaptations of LH incorporate social and environmental comparative parameters such as resource consumption, for instance, reducing operational costs by up to 30–40%. Many challenges were faced with this implementation, such as cultural, technical challenges (e.g., complexity of integration with digital systems), and sustainability barriers. However, to overcome these barriers, this paper proposes a holistic implementation that aligns lean processes with organizational change and sustainability goals. Full article

Median crossover crashes are among the most severe roadway safety events due to their high-energy nature and strong association with fatal and incapacitating injuries, posing a substantial public health burden. This study develops a multimodal intelligent analytics framework to evaluate the cable median barrier performance in Tennessee by integrating structured crash data, roadway and traffic characteristics, post-impact vehicle responses, and unstructured police narratives. Across 6094 crashes on 576 cable barrier segments, 1196 involved barrier impacts and 914 included complete post-impact response information. Deep learning-based text mining using a BERT transformer model was applied to narrative descriptions from fatal, serious injury, and minor injury crashes to extract contextual indicators of loss of control, impact dynamics, and injury mechanisms. Safety effectiveness evaluation using Empirical Bayes methods showed substantial reductions after installation, including a 96% decrease in fatal crashes and an 88% reduction in serious-injury crashes. Vehicle–barrier interactions—classified as containment, redirection, rollover, or penetration—were modeled using a multinomial logit framework with marginal effects to assess the influence of geometric, operational, and vehicle-related factors. Reduced barrier offset, narrow shoulders, high traffic volumes, outer-lane departures, and heavy-vehicle involvement significantly increased the likelihood of rollover and penetration events, which are strongly linked to higher injury severity. Through fusing multimodal data and combining explainable statistical models with deep learning text analysis, this study provided a scalable, trustworthy approach to characterizing injury risk, aligning transportation safety analytics with emerging intelligent healthcare and big-data methodologies aimed at preventing severe and fatal trauma. Full article

As dockless bike-sharing systems rapidly expanded, this study aims to develop a flow-integrated framework for assessing bicycle usage efficiency, which addresses a critical gap in conventional static indicators. Existing studies rely primarily on big data to evaluate location-specific efficiency using Time-to-Booking (ToB). However, ToB ignores network flow effects while bicycles departing from the same location may reach destinations with vastly different ToB values. To overcome this, we propose a flow-integrated ToB (FwToB) index that incorporates the idle time at both the trip origin and destination. Applying this index to central Beijing reveals significant spatial heterogeneity while maintaining the original core-periphery pattern, indicating that most bicycles flow to areas with similar efficiency. Geographically weighted regression further shows that factors like population density, healthcare, shopping facilities, and distance to metro stations influence efficiency with substantial spatial non-stationarity. These findings advance the understanding of bike-sharing efficiency and offer insights for operators and urban planners. Full article

Global health faces unprecedented challenges driven by communicable diseases, which are increasingly amplified by persistent health inequities, the impact of climate change, and the speed of emerging crises. Prevention is not merely a component but the foundational strategy for an effective, sustainable, and fiscally responsible public health response. This paper delves into the pivotal role of core prevention levers: robust vaccination programs, stringent hygiene standards, advanced epidemiological surveillance, and targeted health education. We detail how contemporary technological advancements, including Artificial Intelligence (AI), big data analytics, and genomics, are fundamentally reshaping infectious disease management, enabling superior predictive capabilities, faster early warning systems, and personalized prevention models. Furthermore, we thoroughly examine the imperative of integrating the One Health approach, which formally recognizes the close, interdependent links between human, animal, and environmental health as critical for combating complex threats like zoonoses and Antimicrobial Resistance (AMR). Despite significant scientific progress, persistent socio-economic disparities, the pervasive influence of health-related misinformation (infodemics), and structural weaknesses in global preparedness underscore the urgent need for decisive international cooperation and equitable financing models. We conclude that only through integrated, multidisciplinary, and resource-equitable strategies can the global community ensure effective prevention, mitigate severe socio-economic disruption, and successfully build resilient healthcare systems capable of withstanding future global health threats. Full article

The rapid expansion of health big data, encompassing genomic profiles and wearable device telemetry, has significantly escalated personal privacy risks. This systematic literature review (SLR) synthesizes 86 peer-reviewed studies (2014–2025) through the dual lens of the NIST Cybersecurity and Privacy Frameworks to evaluate emerging risks, mitigation technologies, and regulatory landscapes. Our analysis identifies unauthorized access as the predominant threat, while blockchain-based solutions comprise 22.1% of proposed interventions. However, a comparative evaluation reveals critical performance trade-offs: differential privacy mechanisms incur a 15–35% utility loss, whereas blockchain implementations impose a 40–50% computational overhead. Furthermore, an assessment of major regulatory frameworks (GDPR, HIPAA, PIPL, and emerging regional laws in Sub-Saharan Africa) elucidates significant cross-jurisdictional conflicts. To address these challenges, we propose the Bio-inspired Adaptive Healthcare Privacy (BAHP) framework, validated through retrospective case study analysis, offering a dynamic approach to securing sensitive health ecosystems. Full article