Influencing factors and countermeasures on intelligent transformation and upgrading of logistics firms: A case study in China.

This study explores the influencing factors on intelligent transformation and upgrading of China's logistics firms under smart logistics, and designs the corresponding framework to guide the practice of firms. By analyzing the characteristics of smart logistics and the transformation and upgrading needs of traditional logistics, from the micro perspective of logistics firms, this paper constructs influencing factor index system of smart transformation and development from four dimensions: logistics technology innovation, logistics big data sharing, logistics management upgrading and logistics decision-making transformation. Logistics firms are divided into firms with medium scale and above and small and medium-sized firms according to their scale. Then EWIF-AHP model is proposed to measure the weight of index system and score the decision-making, so as to evaluate the impact of various influencing factors on transformation and development of logistics firms. The results show that, for logistics firms above medium scale, logistics technology innovation and logistics big data sharing have the most significant impact on transformation and development, followed by logistics management upgrading and logistics decision-making transformation. For small and medium-sized logistics firms, the biggest factor is the upgrading of logistics management, followed by the upgrading of logistics technology, which is almost as important as the influencing factors of the upgrading of logistics management, and followed by the sharing of logistics big data and the transformation of logistics decision-making. Therefore, corresponding countermeasures and suggestions for intelligent transformation of logistics firms have been put forward.

With super SDMs (machine learning, open access big data, and the cloud) towards more holistic global squirrel hotspots and coldspots.

Species-habitat associations are correlative, can be quantified, and used for powerful inference. Nowadays, Species Distribution Models (SDMs) play a big role, e.g. using Machine Learning and AI algorithms, but their best-available technical opportunities remain still not used for their potential e.g. in the policy sector. Here we present Super SDMs that invoke ML, OA Big Data, and the Cloud with a workflow for the best-possible inference for the 300 + global squirrel species. Such global Big Data models are especially important for the many marginalized squirrel species and the high number of endangered and data-deficient species in the world, specifically in tropical regions. While our work shows common issues with SDMs and the maxent algorithm ('Shallow Learning'), here we present a multi-species Big Data SDM template for subsequent ensemble models and generic progress to tackle global species hotspot and coldspot assessments for a more inclusive and holistic inference.

Seeing the whole elephant: integrated advanced data analytics in support of RWE for the development and use of innovative pharmaceuticals.

INTRODUCTION: The 21st century has brought about significant technological advancement, allowing the collection of new types of data from the real world on an unprecedented scale. The healthcare industry will benefit immensely from this abundance of patient data from electronic health records (EHR), patient-reported outcomes (PROs), laboratory, demographic, social media, digital, and even climate data. AREAS COVERED: While conventional statistical methods still play a significant role in supporting the drug lifecycle, machine learning (ML) and artificial intelligence (AI) are assuming a more prominent role in the analysis of this 'big data.' Moving forward, conventional statistics and AI/ML will work together to support descriptive, diagnostic, and even predictive analytics to further revolutionize drug discovery and development, regulatory approvals, and payer acceptance. In addition, counterfactual prescriptive analytics, such as causal inference analysis using real-world data (RWD) to generate insights that have cause-and-effect conclusions, will gain momentum as a methodology that can stand up against the rigor of regulatory review. EXPERT OPINION: Our real-world evidence/health economics and outcomes research (RWE/HEOR) field has evolved in ways that require us to integrate all the methods and data into a single framework that guides a holistic analytic approach and decision-making.

How the National Health Insurance Coverage policy changed the use of lenvatinib for adult patients with advanced hepatocellular carcinoma: a retrospective cohort analysis with real world big data.

BACKGROUND: To establish a long-term mechanism to control the cost burden of drugs, the Chinese government organized seven rounds of price negotiations for the national reimbursement drug list (NRDL) from 2016 to the end of 2022. The study aimed to evaluate the impact of the National Health Insurance Coverage (NHIC) policy on the use of lenvatinib as the first-line treatment for advanced hepatocellular carcinoma (HCC) within a specific medical insurance region from the micro perspective of individual patient characteristics. METHODS: The data of HCC patients that received lenvatinib from September 2019 to August 2022 was retrieved from the Medical and Health Big Data Center and longitudinally analyzed. Contingency table chi-square statistics and binary logistic regression analysis were used to compare the differences in the categorical variables. Interrupted time-series (ITS) regression analysis was performed to evaluate the changes in the utilization of lenvatinib over 36 months. Multiple linear regression was used to analyze the impact of receiving lenvatinib on the total hospitalization expenses of hospitalized patients with advanced HCC. RESULTS: A total of 12,659 patients with advanced HCC were included in this study. The usage rate of lenvatinib increased from 6.19% to 15.28% over 36 months (P < 0.001). By controlling the other factors, consistent with this, the probability of patients with advanced HCC receiving lenvatinib increased by 2.72-fold after the implementation of the NHIC policy (OR = 2.720, 95% CI:2.396-3.088, P < 0.001). Older, residency in rural areas, lack of fixed income, treatment at hospitals below the tertiary level, and coverage by urban-rural residents' basic medical insurance (URRBMI) were the factors affecting the use of lenvatinib among patients with advanced HCC (P < 0.05). After the implementation of the NHIC policy, the total hospitalization expenses increased (Beta=-0.040, P < 0.001). However, compared to patients who received lenvatinib, the total hospitalization expenses were higher for those who did not receive the drug (US$5022.07 ± US$5488.70 vs. US$3701.63 ± US$4330.70, Beta = 0.062, P < 0.001). CONCLUSIONS: The NHIC policy has significantly increased the utilization of lenvatinib. In addition, we speculate that establishing multi-level medical insurance systems for economically disadvantaged patients would be beneficial in improving the effectiveness of the NHIC policy in the real world.

[Application progress on data-driven technologies in intelligent manufacturing of traditional Chinese medicine extraction].

The application of new-generation information technologies such as big data, the internet of things(IoT), and cloud computing in the traditional Chinese medicine(TCM)manufacturing industry is gradually deepening, driving the intelligent transformation and upgrading of the TCM industry. At the current stage, there are challenges in understanding the extraction process and its mechanisms in TCM. Online detection technology faces difficulties in making breakthroughs, and data throughout the entire production process is scattered, lacking valuable mining and utilization, which significantly hinders the intelligent upgrading of the TCM industry. Applying data-driven technologies in the process of TCM extraction can enhance the understanding of the extraction process, achieve precise control, and effectively improve the quality of TCM products. This article analyzed the technological bottlenecks in the production process of TCM extraction, summarized commonly used data-driven algorithms in the research and production control of extraction processes, and reviewed the progress in the application of data-driven technologies in the following five aspects: mechanism analysis of the extraction process, process development and optimization, online detection, process control, and production management. This article is expected to provide references for optimizing the extraction process and intelligent production of TCM.

Total Force Fitness: Making Holistic, Integrated Whole-Person Research a DoD Priority.

Total Force Fitness (TFF) was conceived as a holistic framework for building and sustaining Human Performance Optimization for Warfighters and their families. As such, TFF research must also be holistic in nature. During the research breakout, group barriers and challenges to TFF research were discussed, and critical research focus areas were prioritized. The top approaches discussed were (1) using big data to identify best practices and health trajectories; (2) applying community-based participatory research principles to military units; (3) focusing on "Whole-Person," integrative research (physical, behavioral, spiritual, and biological) across the Department of Defense; and, finally, (4) prioritizing key opportunities to advance TFF across the active duty and Reserve/Guard enterprises and their families. The research group noted that coordinated action would be needed to move the prioritized agenda forward. Finally, translating research into action is essential because TFF is a way of honoring our service members as whole persons with careers, goals, and families.

Unlocking big data to understand health services usage and government funding during pregnancy and early childhood, evidence in Australia.

BACKGROUND: Maternity care is a high-volume and high-cost area of health care, which entails various types of service use throughout the course of the pregnancy. Thus, the aim of this study was to explore the most common reasons and related costs of health services used by women and babies from pregnancy to 12-month postbirth. METHODS: We used linked administrative data from one state of Australia, which contained all births in Queensland between 01/07/2017 and 30/06/2018. Descriptive analyses were used to identify the 10 most frequent reasons and related costs for accessing inpatient, outpatient, emergency department, and Medicare services. These are reported separately for women and babies in different periods. RESULTS: We included 58,394 births in our data set. The results have highlighted that there was relatively uniform use of inpatient, outpatient, and Medicare services by women and babies, with the 10 most common services accounting for more than half of the total services accessed. However, the emergency department service use was more diverse. Medicare services accounted for the greatest volume (79.21%) of service events but only 10.21% of the overall funding, compared with inpatient services, which accounted for less volume (3.62%) but the highest amount of overall funding (75.19%). CONCLUSION: Study findings provide empirical evidence about the full spectrum of services used by birthing families and their babies, and could assist health providers and managers to understand the services women and infants actually access during pregnancy, birth, and postbirth.

Correlation Analysis of Persistence and Recurrence of Stroke in Young Patients Based on Big Data in Healthcare.

This study aims to analyze the correlation between the persistence and recurrence of stroke in young patients via big data in healthcare. It provides an in-depth introduction to the background of big data in healthcare and a detailed description of stroke symptoms, so as to better apply the Apriori parallelization algorithm based on compression matrix (PBCM) algorithm against the background of big data in healthcare to analyze it. In our study, patients were randomly divided into 2 groups. By observing the different persistent relationships in the groups, the factors affecting the patients' fasting blood glucose (FBG), glycosylated hemoglobin (HbA1c), blood pressure (BP), blood lipids, alcohol consumption, smoking and so on were analyzed. The National Institute of Health Stroke Scale (NIHSS) score, FBG, HbA1c, triglycerides (TG), high-density lipoprotein (HDL), body mass index (BMI), length of hospital stay, gender and high BP, diabetes, heart disease, smoking and other factors affect the recurrence rate of stroke as they all affect the brain, although they are all statistically different (P < .05). The recurrence of stroke requires more attention in the treatment of stroke.

A Comparison of Hypothesis-Driven and Data-Driven Research: A Case Study in Multimodal Data Science in Gut-Brain Axis Research.

Data science, bioinformatics, and machine learning are the advent and progression of the fourth paradigm of exploratory science. The need for human-supported algorithms to capture patterns in big data is at the center of personalized healthcare and directly related to translational research. This paper argues that hypothesis-driven and data-driven research work together to inform the research process. At the core of these approaches are theoretical underpinnings that drive progress in the field. Here, we present several exemplars of research on the gut-brain axis that outline the innate values and challenges of these approaches. As nurses are trained to integrate multiple body systems to inform holistic human health promotion and disease prevention, nurses and nurse scientists serve an important role as mediators between this advancing technology and the patients. At the center of person-knowing, nurses need to be aware of the data revolution and use their unique skills to supplement the data science cycle from data to knowledge to insight.

Big data and machine learning to tackle diabetes management.

BACKGROUND: Type 2 Diabetes (T2D) diagnosis is based solely on glycaemia, even though it is an endpoint of numerous dysmetabolic pathways. Type 2 Diabetes complexity is challenging in a real-world scenario; thus, dissecting T2D heterogeneity is a priority. Cluster analysis, which identifies natural clusters within multidimensional data based on similarity measures, poses a promising tool to unravel Diabetes complexity. METHODS: In this review, we scrutinize and integrate the results obtained in most of the works up to date on cluster analysis and T2D. RESULTS: To correctly stratify subjects and to differentiate and individualize a preventive or therapeutic approach to Diabetes management, cluster analysis should be informed with more parameters than the traditional ones, such as etiological factors, pathophysiological mechanisms, other dysmetabolic co-morbidities, and biochemical factors, that is the millieu. Ultimately, the above-mentioned factors may impact on Diabetes and its complications. Lastly, we propose another theoretical model, which we named the Integrative Model. We differentiate three types of components: etiological factors, mechanisms and millieu. Each component encompasses several factors to be projected in separate 2D planes allowing an holistic interpretation of the individual pathology. CONCLUSION: Fully profiling the individuals, considering genomic and environmental factors, and exposure time, will allow the drive to precision medicine and prevention of complications.

The sociodemographic characteristics and clinical features of the late-life depression patients: results from the Beijing Anding Hospital mental health big data platform.

BACKGROUND: The sociodemographic characteristics and clinical features of the Late-life depression (LLD) patients in psychiatric hospitals have not been thoroughly studied in China. This study aimed to explore the psychiatric outpatient attendance of LLD patients at a psychiatric hospital in China, with a subgroup analysis, such as with or without anxiety, gender differences. METHODS: This retrospective study examined outpatients with LLD from January 2013 to August 2019 using data in the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) in Beijing Anding Hospital. Age, sex, number of visits, use of drugs and comorbid conditions were extracted from medical records. RESULTS: In a sample of 47,334 unipolar depression patients, 31,854 (67.30%) were women, and 15,480 (32.70%) were men. The main comorbidities of LDD are generalized anxiety disorder (GAD) (83.62%) and insomnia (74.52%).Among patients with unipolar depression, of which benzodiazepines accounted for the largest proportion (77.77%), Selective serotonin reuptake inhibitors (SSRIs) accounted for 59.00%, a noradrenergic and specific serotonergic antidepressant (NaSSAs) accounted for 36.20%. The average cost of each visit was approximately 646.27 yuan, and the cost of each visit was primarily attributed to Western medicine (22.97%) and Chinese herbal medicine (19.38%). For the cost of outpatient visits, depression comorbid anxiety group had a higher average cost than the non-anxiety group (p < 0.05). There are gender differences in outpatient costs, men spend more than women, for western medicine, men spend more than women, for Chinese herbal medicine, women spend more than men (all p < 0.05). The utilization rate of SSRIs and benzodiazepines in female patients is significantly higher than that in male patients (p < 0.05). CONCLUSION: LLD patients are more commonly women than men and more commonly used SSRIs and NaSSAs. Elderly patients with depression often have comorbid generalized anxiety. LLD patients spend most of their visits on medicines, and while the examination costs are lower.

Ethnic Music Inheritance and Environmental Monitoring Using Big Data Analysis from the Cultural Perspective.

Ethnic music has too many expectations due to its significance to the national culture. It serves as a mirror, reflecting all the true characteristics of many geographical areas and ethnic groupings. Instilling national self-confidence and fostering national unity are essential outcomes of this. The optimal design plan for Xinjiang folk music inheritance and environmental monitoring based on big data technology is presented in this study from the standpoint of cultural ecology. Big data technology can categorize users who are interested in Xinjiang ethnic music, and after that, through customized recommendation filtering, consumers may be presented with Xinjiang ethnic music that meets their interests. Last but not least, a simulation test and analysis are performed. The algorithm's accuracy is 7.86% higher than that of the conventional algorithm, according to the simulation data. By studying and calculating the user's behavioral traits and interests, this result demonstrates in detail how the recommender system can display the user's content efficiently. However, there are numerous possibilities and varied contexts for the use of clustering techniques in recommender systems. It is crucially vital for directing the protection of ethnic music and fostering the inheritance and development of ethnic culture to conduct design study on the Xinjiang region's ethnic music heritage and development with cultural ecology as the central guiding principle. This article is from "A comprehensive study of Uygur Muqam music art with Chinese characteristics," which aims to improve the data reserve of the world and Southeast Asia on the research of Chinese Uighur Muqam art. Improve the inheritance and development of music in Xinjiang, China, and provide more detailed data to more scholars. This study adopts qualitative research methods and field survey data. The author proposes to focus on the perspective of cultural ecology, based on the use of big data technology, to improve the inheritance and development of Xinjiang national music.

New Algorithm of Traditional Chinese Medicine and Protection of Intangible Cultural Heritage Based on Big Data Deep Learning.

Traditional Chinese medicine (TCM) is a summary of the diagnosis and treatment experience formed by the working people in the long-term struggle against diseases, so it is very important to protect the intangible cultural heritage of TCM. How to extract valuable knowledge accurately and conveniently from the massive medical records of TCM is one of the important issues in the current research on the development of TCM. Due to the large amount of data of TCM medical records, many feature attributes, and diverse patterns, the existing classification technology has high computational complexity, low mining efficiency, and poor universality. Therefore, this paper proposed to quantify the medical records of TCM and obtained the main symptoms according to the improved hierarchical clustering feature selection algorithm. This paper also proposed a support vector machine (SVM) classification method using improved particle swarm algorithm to classify TCM information, which not only improves the efficiency and accuracy of TCM information classification but also discovers the potential dialectical and symptom patterns in diagnosis and treatment, so that the intangible cultural heritage protection of TCM can be developed sustainably. This paper showed that the information acquisition accuracy of the improved algorithm was very high. Before the improved algorithm was used, the accuracy of information mining for TCM was 67.90% at the highest and 65.53% at the lowest, but after using the improved algorithm, the accuracy rate of information mining for TCM was 88.02% at the highest and 82.45% at the lowest. It can be seen that using the improved algorithm to mine TCM information can quickly process effective information.

Evaluation and Analysis of Traditional Customary Law Based on the Perspective of Big Data.

Today's rule of law construction in China is walking between the conflict and coordination of factors such as reality and ideals, tradition and modernity, local and foreign, and local knowledge and universal principles, all while continuing to strengthen the unification of the legal system and advance the modernization of the rule of law. Traditional customary law, which is the most representative local resource culture, is unquestionably one of the most important themes in the formation of the rule of law. It has far-reaching significance for the development of ethnic jurisprudence, the reunderstanding of traditional culture, and the construction of ethnic unity and harmonious society. Based on this background, this paper uses big data technology to collect relevant experimental data and proposes a traditional customary law value assessment based on BPNN. The completed work is as follows: (1) this paper clarifies the concept of customary law and the difference between it and related concepts and introduces the domestic relevant research on traditional customary law and the interactive relationship between customary law and national law in dynamic legal practice and puts forward the status and influence of customary law in contemporary legal practice. (2) The related technologies of neural network are introduced, and a traditional customary value evaluation system that can be used for experiments is constructed. (3) Experiment with the designed data set to see if the BP model is feasible. The experimental results suggest that the model proposed in this study has a low error rate and performs well while evaluating traditional common law values.

Can unsupervised learning methods applied to milk recording big data provide new insights into dairy cow health?

Among the dairy sector's current concerns, the assessment of global animal health status is a complex challenge. Its multidimensionality means that global monitoring tools are rarely considered. Instead, specific disease detection is often studied separately and, due to financial and ethical issues, uses small-scale data sets focusing on few biomarkers. Several studies have already been conducted using milk Fourier transform mid-infrared (FT-MIR) spectroscopy to detect mastitis and lameness or to quantify health-related biomarkers in milk or blood. Those studies are relevant but they focus mainly on one biomarker or disease. To solve this issue and the small-scale data set, in this study, we proposed a holistic approach using big data obtained from milk recording, including milk yield, somatic cell count, and 27 FT-MIR-based predictors related to milk composition and animal health status. Using 740,454 records collected from 114,536 first-parity Holstein cows in southern Belgium, we performed repeated unsupervised learning algorithms based on Ward's agglomerative hierarchical clustering method to find potential interesting patterns. A divide-and-conquer approach was used to overcome the limitation of computational resources in clustering a relatively large data set. Five groups of records were identified. Differences observed in the fourth group suggested a relationship to metabolic disorders. The fifth group seemed to be related to mastitis. In a second step, we performed a partial least squares discriminant analysis (PLS-DA) to predict the probability of belonging to those specific groups for the entire data set. The obtained global accuracy was 0.77 and the balanced accuracy (i.e., the mean between sensitivity and specificity) of discriminating the fourth and fifth groups was 0.88 and 0.96, respectively. Then, a validation of the interpretation of those groups was performed using 204 milk and blood reference records. The predicted probability associated with the metabolic disorders issue had significant correlations of 0.54 with blood ß-hydroxybutyrate, 0.44 with blood nonesterified fatty acids, -0.32 with blood glucose, -0.23 with milk glucose-6-phosphate, and 0.38 with milk isocitrate. In contrast, the predicted probability of belonging to the mastitis group had correlations of 0.69 with milk lactate dehydrogenase, 0.46 with milk N-acetyl-ß-d-glucosaminidase, -0.18 with milk free glucose, and 0.16 with milk glucose-6-phosphate. Consequently, these results suggest that the obtained quantitative traits indirectly reflect some of the main health disorders in dairy farming and could be used to monitor dairy cows on a large scale. By using unsupervised learning on large-scale milk recording data and then validating the pattern using reference laboratory measures, we propose a new approach to quickly assess dairy cow health status.

Infectious diseases prevention and control using an integrated health big data system in China.

BACKGROUND: The Yinzhou Center for Disease Prevention and Control (CDC) in China implemented an integrated health big data platform (IHBDP) that pooled health data from healthcare providers to combat the spread of infectious diseases, such as dengue fever and pulmonary tuberculosis (TB), and to identify gaps in vaccination uptake among migrant children. METHODS: IHBDP is composed of medical data from clinics, electronic health records, residents' annual medical checkup and immunization records, as well as administrative data, such as student registries. We programmed IHBDP to automatically scan for and detect dengue and TB carriers, as well as identify migrant children with incomplete immunization according to a comprehensive set of screening criteria developed by public health and medical experts. We compared the effectiveness of the big data screening with existing traditional screening methods. RESULTS: IHBDP successfully identified six cases of dengue out of a pool of 3972 suspected cases, whereas the traditional method only identified four cases (which were also detected by IHBDP). For TB, IHBDP identified 288 suspected cases from a total of 43,521 university students, in which three cases were eventually confirmed to be TB carriers through subsequent follow up CT or T-SPOT.TB tests. As for immunization screenings, IHBDP identified 240 migrant children with incomplete immunization, but the traditional door-to-door screening method only identified 20 ones. CONCLUSIONS: Our study has demonstrated the effectiveness of using IHBDP to detect both acute and chronic infectious disease patients and identify children with incomplete immunization as compared to traditional screening methods.

Rehabilitation of Sepsis Patients with Acute Kidney Injury Based on Intelligent Medical Big Data.

The objective of this study was to explore rehabilitation of patients with acute kidney injury (AKI) treated with Xuebijing injection by using intelligent medical big data analysis system. Based on Hadoop distributed processing technology, this study designed a medical big data analysis system and tested its performance. Then, this analysis system was used to systematically analyze rehabilitation of sepsis patients with AKI treated with Xuebijing injection. It is found that the computing time of this system does not increase obviously with the increase of cases. The results of systematic analysis showed that the glomerular filtration rate (59.31 ± 3.87% vs 44.53 ± 3.53%) in the experimental group was obviously superior than that in the controls after one week of treatment. The levels of urea nitrogen (9.32 ± 2.21 mmol/L vs. 14.32 ± 0.98 mmol/L), cystatin C (1.65 ± 0.22 mg/L vs. 2.02 ± 0.13 mg/L), renal function recovery time (6.12 ± 1.66 days vs. 8.66 ± 1.17 days), acute physiology and chronic health evaluation system score (8.98 ± 2.12 points vs. 12.45 ± 2.56 points), sequential organ failure score (7.22 ± 0.86 points vs. 8.61 ± 0.97 points), traditional Chinese medicine (TCM) syndrome score (6.89 ± 1.11 points vs. 11.33 ± 1.23 points), and ICU time (16.43 ± 2.37 days vs. 12.15 ± 2.56 days) in the experimental group were obviously lower than those in the controls, and the distinctions had statistical significance (P < 0.05). The significant efficiency (37.19% vs. 25.31%) and total effective rate (89.06% vs. 79.06%) in the experimental group were obviously superior than those in the controls, and distinction had statistical significance (P < 0.05). In summary, the medical big data analysis system constructed in this study has high efficiency. Xuebijing injection can improve the renal function of sepsis patients with kidney injury, and its therapeutic effect is obviously better than that of Western medicine, and it has clinical application and promotion value.

Socio-demographic factors and lifestyle associated with symptomatic hemorrhoids: Big data analysis using the National Health insurance Service-National Health screening cohort (NHIS-HEALS) database in Korea.

OBJECTIVE: The prevalence of hemorrhoids has been reported to be 7-14%. However, there have been no large-scale studies. This study aims to investigate the incidence of hemorrhoids in Korea by analyzing big data and to find the associated risk factors. METHODS: This was a retrospective analysis using the Health Insurance Cohort database of the National Health Insurance Corporation of Korea in 2002-2015. The study was divided into two models: the diagnostic (DM) and surgical model (SM). Socio-demographic and lifestyle behavioral characteristics were analyzed as risk factors. RESULTS: Overall, 467,567 participants were included. The incidence density of hemorrhoids was 13.9 and 5.7 per 1000 person-years in the DM and SM, respectively. Hemorrhoids occurred more frequently in men and metropolitan areas in both models. The incidence was highest in the 40s. The incidence rates were highest in the high income, smoking, alcohol and the exercise group of 1-4 times a week in both models. The adjusted hazard ratio (HR) was higher in men and decreased with increasing age. It was higher in the metropolitan area. The high-income level and alcohol consumption were risk factors in the DM and SM, respectively. The HR of the exercise group was higher than that of the non-exercise group in both models. CONCLUSIONS: The diagnostic and surgical incidence density was 13.9 and 5.7 per 1000 person-years, respectively. Hemorrhoids occurred most frequently in men in their 40s. The metropolitan area, high income level and alcohol consumption were associated with an increased frequency of hemorrhoids.

Neural Network-Oriented Big Data Model for Yoga Movement Recognition.

The use of computer vision for target detection and recognition has been an interesting and challenging area of research for the past three decades. Professional athletes and sports enthusiasts in general can be trained with appropriate systems for corrective training and assistive training. Such a need has motivated researchers to combine artificial intelligence with the field of sports to conduct research. In this paper, we propose a Mask Region-Convolutional Neural Network (MR-CNN)- based method for yoga movement recognition based on the image task of yoga movement recognition. The improved MR-CNN model is based on the framework and structure of the region-convolutional network, which proposes a certain number of candidate regions for the image by feature extraction and classifies them, then outputs these regions as detected bounding boxes, and does mask prediction for the candidate regions using segmentation branches. The improved MR-CNN model uses an improved deep residual network as the backbone network for feature extraction, bilinear interpolation of the extracted candidate regions using Region of Interest (RoI) Align, followed by target classification and detection, and segmentation of the image using the segmentation branch. The model improves the convolution part in the segmentation branch by replacing the original standard convolution with a depth-separable convolution to improve the network efficiency. Experimentally constructed polygon-labeled datasets are simulated using the algorithm. The deepening of the network and the use of depth-separable network improve the accuracy of detection while maintaining the reliability of the network and validate the effectiveness of the improved MR-CNN.

[Big Data in health: a new paradigm to regulate, a challenge for social justice.] / Big Data en salud: Un nuevo paradigma para regular, un desafío para la justicia social.

In addition to the opportunities posed by the use of Big Data in health, it also generates important challenges in the field of research, especially from the point of view of its management and ethical considerations. The European Union has been promoting different initiatives that allow the exploitation of this data in the context of the knowledge economy. The UNESCO Ethics Committee has identified three ethical principles to take into account regarding the application of Big Data in Health: independence, privacy and justice. The protection of privacy and patient safety is questioned in a context where cybersecurity is far to be complete. In addition, an imbalance in the exploitation of these data by the public and private sectors could generate inequalities that would represent a significant problem of social justice. This article follows a qualitative methodology based on the documentary analysis of current legislative texts, especially the recently approved General Data Protection Regulation (GDPR), as well as non-legislative documents of projects and parliamentary communications throughout the last two legislatures, with the aim of analyzing them and evaluating how they conform to the principles outlined by UNESCO, especially with respect to the principle of social justice. The most representative national projects that have started to be adopted are also reviewed.

Además de las oportunidades que supone el uso de Big Data en salud, también genera desafíos importantes en el campo de la investigación, especialmente desde el punto de vista de su gestión y de las consideraciones éticas. La Unión Europea ha estado promoviendo diferentes iniciativas que permitan la explotación de estos datos en el contexto de la economía del conocimiento. El Comité de Ética de la UNESCO ha identificado tres principios éticos a tener en cuenta sobre la aplicación de Big Data en Salud: independencia, privacidad y justicia. La protección de la privacidad y la seguridad de los pacientes se cuestiona en un contexto en el que la ciberseguridad está lejos de ser completa. Además, un desequilibrio en la explotación de estos datos por parte de los sectores público y privado podría generar inequidades que significarían un problema importante de justicia social. Este artículo sigue una metodología cualitativa basada en el análisis documental de los textos legislativos vigentes, especialmente el recientemente aprobado reglamento general de protección de datos (RGPD), así como documentos no legislativos de proyectos y comunicaciones parlamentarias a lo largo de las dos últimas legislaturas, con el objetivo es analizarlas y evaluar cómo se ajustan a los principios esbozados por la UNESCO, especialmente con respecto al principio de justicia social. También se revisan los proyectos nacionales más representativos que han empezado a adoptarse.