Quality of information for quality of life: Healthcare big data analytics

Big data analysis in healthcare sector is still in its early stages when comparing with that of other business sectors due to numerous reasons. Accommodating the volume, velocity and variety of healthcare data Identifying platforms that examine data from multiple sources, such as clinical records, genomic data, financial systems, and administrative systems Electronic Health Record (EHR) is a key information resource for big data analysis and is also composed of varied co-created values. Successful integration and crossing of different subfields of healthcare data such as biomedical informatics and health informatics could lead to huge improvement for the end users of the health care system, i.e. the patients.

Information Accountability of Healthcare Big Data

Huge amount of data are generated from a variety of information sources in healthcare while the data sources originate from a veracity of clinical information systems and corporate data warehouses. The data derived from the above data sources are used for analysis and trending purposes thus playing an influential role as a real time decision-making tool. The unstructured, narrative data provided by these data sources qualify as healthcare big-data and researchers argue that the application of big-data in healthcare might enable the accountability and efficiency.

Screen production enquiry: A study of five Australian doctorates

Within Australian universities, doctoral research in screen production is growing significantly. Two recent studies have documented both the scale of this research and inconsistencies in the requirements of the degree. These institutional variations, combined with a lack of clarity around appropriate methodologies for academic research through film and television practice, create challenges for students, supervisors, examiners and the overall development of the discipline. This paper will examine five recent doctorates in screen production practice at five different Australian universities. It will look at the nature of the films made, the research questions the candidates were investigating, the new knowledge claims that were produced and the subsequent impact of the research. The various methodologies used will be given particular attention because they help define the nature of the research where film production is a primary research method.

Adopting healthcare big data in Sri Lankan healthcare sector

The concept of big data has already outperformed traditional data management efforts in almost all industries. Other instances it has succeeded in obtaining promising results that provide value from large-scale integration and analysis of heterogeneous data sources for example Genomic and proteomic information. Big data analytics have become increasingly important in describing the data sets and analytical techniques in software applications that are so large and complex due to its significant advantages including better business decisions, cost reduction and delivery of new product and services [1]. In a similar context, the health community has experienced not only more complex and large data content, but also information systems that contain a large number of data sources with interrelated and interconnected data attributes. That have resulted in challenging, and highly dynamic environments leading to creation of big data with its enumerate complexities, for instant sharing of information with the expected security requirements of stakeholders. When comparing big data analysis with other sectors, the health sector is still in its early stages. Key challenges include accommodating the volume, velocity and variety of healthcare data with the current deluge of exponential growth. Given the complexity of big data, it is understood that while data storage and accessibility are technically manageable, the implementation of Information Accountability measures to healthcare big data might be a practical solution in support of information security, privacy and traceability measures. Transparency is one important measure that can demonstrate integrity which is a vital factor in the healthcare service. Clarity about performance expectations is considered to be another Information Accountability measure which is necessary to avoid data ambiguity and controversy about interpretation and finally, liability [2]. According to current studies [3] Electronic Health Records (EHR) are key information resources for big data analysis and is also composed of varied co-created values [3]. Common healthcare information originates from and is used by different actors and groups that facilitate understanding of the relationship for other data sources. Consequently, healthcare services often serve as an integrated service bundle. Although a critical requirement in healthcare services and analytics, it is difficult to find a comprehensive set of guidelines to adopt EHR to fulfil the big data analysis requirements. Therefore as a remedy, this research work focus on a systematic approach containing comprehensive guidelines with the accurate data that must be provided to apply and evaluate big data analysis until the necessary decision making requirements are fulfilled to improve quality of healthcare services. Hence, we believe that this approach would subsequently improve quality of life.

Information accountability and health big data analytics: A consent-based model

With the ever increasing amount of eHealth data available from various eHealth systems and sources, Health Big Data Analytics promises enticing benefits such as enabling the discovery of new treatment options and improved decision making. However, concerns over the privacy of information have hindered the aggregation of this information. To address these concerns, we propose the use of Information Accountability protocols to provide patients with the ability to decide how and when their data can be shared and aggregated for use in big data research. In this paper, we discuss the issues surrounding Health Big Data Analytics and propose a consent-based model to address privacy concerns to aid in achieving the promised benefits of Big Data in eHealth.

Five years of GWAS discovery

The past five years have seen many scientific and biological discoveries made through the experimental design of genome-wide association studies (GWASs). These studies were aimed at detecting variants at genomic loci that are associated with complex traits in the population and, in particular, at detecting associations between common single-nucleotide polymorphisms (SNPs) and common diseases such as heart disease, diabetes, auto-immune diseases, and psychiatric disorders. We start by giving a number of quotes from scientists and journalists about perceived problems with GWASs. We will then briefly give the history of GWASs and focus on the discoveries made through this experimental design, what those discoveries tell us and do not tell us about the genetics and biology of complex traits, and what immediate utility has come out of these studies. Rather than giving an exhaustive review of all reported findings for all diseases and other complex traits, we focus on the results for auto-immune diseases and metabolic diseases. We return to the perceived failure or disappointment about GWASs in the concluding section. © 2012 The American Society of Human Genetics.

Arson-associated homicide in Australia: A five year follow-up

Arson homicides are rare, representing only two percent of all homicides in Australia each year. In this study, data was collected from the AIC’s National Homicide Monitoring Program (NHMP) to build on previous research undertaken into arson-associated homicides (Davies & Mouzos 2007) and to provide more detailed analysis of cases and offenders. Over the period 1989 to 2010, there were 123 incidents of arson-associated homicide, involving 170 unique victims and 131 offenders. The majority of incidents (63%) occurred in the victim’s home and more than half (57%) of all victims were male. It was found that there has been a 44 percent increase in the number of incidents in the past decade. It is evident that a considerable proportion of the identified arson homicides involved a high degree of premeditation and planning. These homicides were commonly committed by an offender who was well known to the victim, with over half of the victims (56%) specifically targeted by the offender. This paper therefore provides a valuable insight into the nature of arson homicides and signposts areas for further investigation.

A principled experimental design approach to Big Data analysis

Big Datasets are endemic, but they are often notoriously difficult to analyse because of their size, heterogeneity, history and quality. The purpose of this paper is to open a discourse on the use of modern experimental design methods to analyse Big Data in order to answer particular questions of interest. By appealing to a range of examples, it is suggested that this perspective on Big Data modelling and analysis has wide generality and advantageous inferential and computational properties. In particular, the principled experimental design approach is shown to provide a flexible framework for analysis that, for certain classes of objectives and utility functions, delivers near equivalent answers compared with analyses of the full dataset under a controlled error rate. It can also provide a formalised method for iterative parameter estimation, model checking, identification of data gaps and evaluation of data quality. Finally, it has the potential to add value to other Big Data sampling algorithms, in particular divide-and-conquer strategies, by determining efficient sub-samples.

FlexAnalytics: A flexible data analytics framework for big data applications with I/O performance improvement

Increasingly larger scale applications are generating an unprecedented amount of data. However, the increasing gap between computation and I/O capacity on High End Computing machines makes a severe bottleneck for data analysis. Instead of moving data from its source to the output storage, in-situ analytics processes output data while simulations are running. However, in-situ data analysis incurs much more computing resource contentions with simulations. Such contentions severely damage the performance of simulation on HPE. Since different data processing strategies have different impact on performance and cost, there is a consequent need for flexibility in the location of data analytics. In this paper, we explore and analyze several potential data-analytics placement strategies along the I/O path. To find out the best strategy to reduce data movement in given situation, we propose a flexible data analytics (FlexAnalytics) framework in this paper. Based on this framework, a FlexAnalytics prototype system is developed for analytics placement. FlexAnalytics system enhances the scalability and flexibility of current I/O stack on HEC platforms and is useful for data pre-processing, runtime data analysis and visualization, as well as for large-scale data transfer. Two use cases – scientific data compression and remote visualization – have been applied in the study to verify the performance of FlexAnalytics. Experimental results demonstrate that FlexAnalytics framework increases data transition bandwidth and improves the application end-to-end transfer performance.

A CCG virtual system for big data application communication costs analysis

Network topology and routing are two important factors in determining the communication costs of big data applications at large scale. As for a given Cluster, Cloud, or Grid system, the network topology is fixed and static or dynamic routing protocols are preinstalled to direct the network traffic. Users cannot change them once the system is deployed. Hence, it is hard for application developers to identify the optimal network topology and routing algorithm for their applications with distinct communication patterns. In this study, we design a CCG virtual system (CCGVS), which first uses container-based virtualization to allow users to create a farm of lightweight virtual machines on a single host. Then, it uses software-defined networking (SDN) technique to control the network traffic among these virtual machines. Users can change the network topology and control the network traffic programmingly, thereby enabling application developers to evaluate their applications on the same system with different network topologies and routing algorithms. The preliminary experimental results through both synthetic big data programs and NPB benchmarks have shown that CCGVS can represent application performance variations caused by network topology and routing algorithm.

The first application of wastewater-based drug epidemiology in five South Korean cities

Illicit drug consumption in five cities in South Korea was estimated by analyzing 17 drug residues in untreated wastewater samples collected during the Christmas and New Year period of 2012-13. Only methamphetamine, amphetamine, and codeine were detected at concentrations of tens of nanograms per liter or even lower concentrations in more than 90% of the samples. Other illicit drug residues (including cocaine, methadone, and benzoylecgonine) that have been detected frequently in wastewater from other countries were not found in this study. Methamphetamine was found to be the most widely used illicit drug in South Korea, and the estimated average consumption rate was 22 mg d−1 (1000 people)−1. This rate is, for example, 2-5 times lower than the estimated average consumption rates in Hong Kong and other parts of China and 4-80 times lower than the estimated average consumption rates in cities in Western countries. It should be noted that the wastewater samples analyzed in this study were collected during a holiday season, when daily consumption of illicit drugs is often higher than on an average day. The methamphetamine usage rates were calculated for different cities in South Korea, and the usage rates in smaller cities was higher (2-4 times) than the average.

Dioxin risk reduction programme at the most severe dioxin hot spot in Vietnam: Residual knowledge, attitude and practices five years post intervention

Bien Hoa Airbase was one of the bulk storage and supply facilities for defoliants during the Vietnam War. Environmental and biological samples taken around the airbase have elevated levels of dioxin. In 2007, a pre-intervention knowledge, attitude and practice (KAP) survey of local residents living in Trung Dung and Tan Phong wards was undertaken regarding appropriate strategies to reduce dioxin exposure. A risk reduction programme was implemented in 2008 and post-intervention KAP surveys were undertaken in 2009 and 2013 to evaluate the longer term impacts. Quantitative assessment was undertaken via a KAP survey in 2013 among 600 local residents randomly selected from the two intervention wards and one control ward (Buu Long). Eight in-depth interviews and two focus group discussions were also undertaken for qualitative assessment. Most programme activities had ceased and dioxin risk communication activities had not been integrated into local routine health education programmes; however, main results generally remained and were better than that in Buu Long. In total, 48.2% of households undertook measures to prevent exposure, higher than those in pre- and post-intervention surveys (25.8% and 39.7%) and the control ward (7.7%). Migration and the sensitive nature of dioxin issues were the main challenges for the programme's sustainability

Genome-wide association study identifies five new schizophrenia loci

We examined the role of common genetic variation in schizophrenia in a genome-wide association study of substantial size: a stage 1 discovery sample of 21,856 individuals of European ancestry and a stage 2 replication sample of 29,839 independent subjects. The combined stage 1 and 2 analysis yielded genome-wide significant associations with schizophrenia for seven loci, five of which are new (1p21.3, 2q32.3, 8p23.2, 8q21.3 and 10q24.32-q24.33) and two of which have been previously implicated (6p21.32-p22.1 and 18q21.2). The strongest new finding (P = 1.6 × 10 -11) was with rs1625579 within an intron of a putative primary transcript for MIR137 (microRNA 137), a known regulator of neuronal development. Four other schizophrenia loci achieving genome-wide significance contain predicted targets of MIR137, suggesting MIR137-mediated dysregulation as a previously unknown etiologic mechanism in schizophrenia. In a joint analysis with a bipolar disorder sample (16,374 affected individuals and 14,044 controls), three loci reached genome-wide significance: CACNA1C (rs4765905, P = 7.0 × 10 -9), ANK3 (rs10994359, P = 2.5 × 10 -8) and the ITIH3-ITIH4 region (rs2239547, P = 7.8 × 10 -9).

Assistive classification for improving the efficiency of avian species richness surveys

Avian species richness surveys, which measure the total number of unique avian species, can be conducted via remote acoustic sensors. An immense quantity of data can be collected, which, although rich in useful information, places a great workload on the scientists who manually inspect the audio. To deal with this big data problem, we calculated acoustic indices from audio data at a one-minute resolution and used them to classify one-minute recordings into five classes. By filtering out the non-avian minutes, we can reduce the amount of data by about 50% and improve the efficiency of determining avian species richness. The experimental results show that, given 60 one-minute samples, our approach enables to direct ecologists to find about 10% more avian species.