Research on Parallel Support Vector Machine Based on Spark Big Data Platform

With the development of cloud computing and distributed cluster technology, the concept of big data has been expanded and extended in terms of capacity and value, and machine learning technology has also received unprecedented attention in recent years. Traditional machine learning algorithms cannot solve the problem of effective parallelization, so a parallelization support vector machine based on Spark big data platform is proposed. Firstly, the big data platform is designed with Lambda architecture, which is divided into three layers: Batch Layer, Serving Layer, and Speed Layer. Secondly, in order to improve the training efficiency of support vector machines on large-scale data, when merging two support vector machines, the “special points” other than support vectors are considered, that is, the points where the nonsupport vectors in one subset violate the training results of the other subset, and a cross-validation merging algorithm is proposed. Then, a parallelized support vector machine based on cross-validation is proposed, and the parallelization process of the support vector machine is realized on the Spark platform. Finally, experiments on different datasets verify the effectiveness and stability of the proposed method. Experimental results show that the proposed parallelized support vector machine has outstanding performance in speed-up ratio, training time, and prediction accuracy.

COVID-19 In-Hospital Mortality in People with Diabetes Is Driven by Comorbidities and Age—Propensity Score-Matched Analysis of Austrian National Public Health Institute Data

Background: It is a matter of debate whether diabetes alone or its associated comorbidities are responsible for severe COVID-19 outcomes. This study assessed the impact of diabetes on intensive care unit (ICU) admission and in-hospital mortality in hospitalized COVID-19 patients. Methods: A retrospective analysis was performed on a countrywide cohort of 40,632 COVID-19 patients hospitalized between March 2020 and March 2021. Data were provided by the Austrian data platform. The association of diabetes with outcomes was assessed using unmatched and propensity-score matched (PSM) logistic regression. Results: 12.2% of patients had diabetes, 14.5% were admitted to the ICU, and 16.2% died in the hospital. Unmatched logistic regression analysis showed a significant association of diabetes (odds ratio [OR]: 1.24, 95% confidence interval [CI]: 1.15–1.34, p < 0.001) with in-hospital mortality, whereas PSM analysis showed no significant association of diabetes with in-hospital mortality (OR: 1.08, 95%CI: 0.97–1.19, p = 0.146). Diabetes was associated with higher odds of ICU admissions in both unmatched (OR: 1.36, 95%CI: 1.25–1.47, p < 0.001) and PSM analysis (OR: 1.15, 95%CI: 1.04–1.28, p = 0.009). Conclusions: People with diabetes were more likely to be admitted to ICU compared to those without diabetes. However, advanced age and comorbidities rather than diabetes itself were associated with increased in-hospital mortality in COVID-19 patients.

Detection and Localization of Failures in Hybrid Fiber–Coaxial Network Using Big Data Platform

Modern HFC (Hybrid Fiber–Coaxial) networks comprise millions of users. It is of great importance for HFC network operators to provide high network access availability to their users. This requirement is becoming even more important given the increasing trend of remote working. Therefore, network failures need to be detected and localized as soon as possible. This is not an easy task given that there is a large number of devices in typical HFC networks. However, the large number of devices also enable HFC network operators to collect enormous amounts of data that can be used for various purposes. Thus, there is also a trend of introducing big data technologies in HFC networks to be able to efficiently cope with the huge amounts of data. In this paper, we propose a novel mechanism for efficient failure detection and localization in HFC networks using a big data platform. The proposed mechanism utilizes the already present big data platform and collected data to add one more feature to big data platform—efficient failure detection and localization. The proposed mechanism has been successfully deployed in a real HFC network that serves more than one million users.

Application of Internet and Big Data Technology to Give Full Play to the Function of Procuratorial Civil Public Interest Litigation in China

The procuratorial civil public interest litigation system is a kind of legal system, which will realize certain legal functions. As an important way for procuratorial organs to exercise their functions and powers, procuratorial civil public interest litigation system in China has many functions: on the one hand, it has the core function of protecting social public interests; on the other hand, it has the main function of enforcing laws, forming public policies and promoting social governance; and it has the guiding function of providing reference for similar reforms. How to fully give play to the function of procuratorial civil public interest litigation, the big data is an important means. Procuratorial organs should make full use of the information of data platform, and enhance the joint efforts of public welfare protection, and set up the thinking of handling cases with information and improve application ability, so as to plug in "wisdom wings" for the procuratorial civil public interest litigation.

Utilising the FOXS Stack for FAIR Architected Data Access

A FOXS stack assembles HL7 FHIR, openEHR, IHE XDS and SNOMED CT as an operational clinical data platform to build digital systems. This paper analyses its applicability for FAIR-enabled medical research based on a summary of key principles. It highlights the benefit of the blended approach to operational technology stacks for health systems, and a need for industry standard technologies to enable greater semantic coherence for primary/secondary data use.

The hospital burden of intergenerational contact with the welfare system: A whole-of-population liked data study

Background: Intergenerational welfare contact (IWC) is a policy issue because of the personal and social costs of intergenerational disadvantage. We estimated the hospital burden of IWC for children aged 11-20 years. Methods: This linked data study of children born in South Australia, 1991-1995 (n=94,358), and their parent/s (n=143,814) used de-identified data from the Better Evidence Better Outcomes Linked Data platform. Using Australian Government Centrelink data, welfare contact (WC) was defined as parent/s receiving a means-tested welfare payment (low-income, unemployment, disability or caring) when children were aged 11-15, or children receiving payment at ages 16-20. IWC was WC occurring in both parent and child generations. Children were classified as: No WC, parent only WC, child only WC, or IWC. Hospitalisation rates and cumulative incidence were estimated by age and WC group. Findings: IWC affected 34.9% of children, who had the highest hospitalisation rate (133.5 per 1,000 person-years) compared to no WC (46.1 per 1,000 person-years), parent only WC (75.0 per 1,000 person-years), and child only WC (87.6 per 1,000 person-years). Of all IWC children, 43.0% experienced at least one hospitalisation between 11-20, frequently related to injury, mental health, and pregnancy. Interpretation: Children experiencing IWC represent a third of the population aged 11-20. Compared to children with parent-only WC, IWC children had 78% higher hospitalisation rates from age 11 to 20, accounting for over half of all hospitalisations in this age group. Frequent IWC hospitalisation causes were injuries, mental health, and pregnancy. Funding: Medical Research Future Fund, National Health and Medical Research Council, Westpac Scholars Trust.