12 resultados para Healthcare Big Data Analytics

em CORA - Cork Open Research Archive - University College Cork - Ireland


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A substantial amount of information on the Internet is present in the form of text. The value of this semi-structured and unstructured data has been widely acknowledged, with consequent scientific and commercial exploitation. The ever-increasing data production, however, pushes data analytic platforms to their limit. This thesis proposes techniques for more efficient textual big data analysis suitable for the Hadoop analytic platform. This research explores the direct processing of compressed textual data. The focus is on developing novel compression methods with a number of desirable properties to support text-based big data analysis in distributed environments. The novel contributions of this work include the following. Firstly, a Content-aware Partial Compression (CaPC) scheme is developed. CaPC makes a distinction between informational and functional content in which only the informational content is compressed. Thus, the compressed data is made transparent to existing software libraries which often rely on functional content to work. Secondly, a context-free bit-oriented compression scheme (Approximated Huffman Compression) based on the Huffman algorithm is developed. This uses a hybrid data structure that allows pattern searching in compressed data in linear time. Thirdly, several modern compression schemes have been extended so that the compressed data can be safely split with respect to logical data records in distributed file systems. Furthermore, an innovative two layer compression architecture is used, in which each compression layer is appropriate for the corresponding stage of data processing. Peripheral libraries are developed that seamlessly link the proposed compression schemes to existing analytic platforms and computational frameworks, and also make the use of the compressed data transparent to developers. The compression schemes have been evaluated for a number of standard MapReduce analysis tasks using a collection of real-world datasets. In comparison with existing solutions, they have shown substantial improvement in performance and significant reduction in system resource requirements.

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It is estimated that the quantity of digital data being transferred, processed or stored at any one time currently stands at 4.4 zettabytes (4.4 × 2 70 bytes) and this figure is expected to have grown by a factor of 10 to 44 zettabytes by 2020. Exploiting this data is, and will remain, a significant challenge. At present there is the capacity to store 33% of digital data in existence at any one time; by 2020 this capacity is expected to fall to 15%. These statistics suggest that, in the era of Big Data, the identification of important, exploitable data will need to be done in a timely manner. Systems for the monitoring and analysis of data, e.g. stock markets, smart grids and sensor networks, can be made up of massive numbers of individual components. These components can be geographically distributed yet may interact with one another via continuous data streams, which in turn may affect the state of the sender or receiver. This introduces a dynamic causality, which further complicates the overall system by introducing a temporal constraint that is difficult to accommodate. Practical approaches to realising the system described above have led to a multiplicity of analysis techniques, each of which concentrates on specific characteristics of the system being analysed and treats these characteristics as the dominant component affecting the results being sought. The multiplicity of analysis techniques introduces another layer of heterogeneity, that is heterogeneity of approach, partitioning the field to the extent that results from one domain are difficult to exploit in another. The question is asked can a generic solution for the monitoring and analysis of data that: accommodates temporal constraints; bridges the gap between expert knowledge and raw data; and enables data to be effectively interpreted and exploited in a transparent manner, be identified? The approach proposed in this dissertation acquires, analyses and processes data in a manner that is free of the constraints of any particular analysis technique, while at the same time facilitating these techniques where appropriate. Constraints are applied by defining a workflow based on the production, interpretation and consumption of data. This supports the application of different analysis techniques on the same raw data without the danger of incorporating hidden bias that may exist. To illustrate and to realise this approach a software platform has been created that allows for the transparent analysis of data, combining analysis techniques with a maintainable record of provenance so that independent third party analysis can be applied to verify any derived conclusions. In order to demonstrate these concepts, a complex real world example involving the near real-time capturing and analysis of neurophysiological data from a neonatal intensive care unit (NICU) was chosen. A system was engineered to gather raw data, analyse that data using different analysis techniques, uncover information, incorporate that information into the system and curate the evolution of the discovered knowledge. The application domain was chosen for three reasons: firstly because it is complex and no comprehensive solution exists; secondly, it requires tight interaction with domain experts, thus requiring the handling of subjective knowledge and inference; and thirdly, given the dearth of neurophysiologists, there is a real world need to provide a solution for this domain

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The organisational decision making environment is complex, and decision makers must deal with uncertainty and ambiguity on a continuous basis. Managing and handling decision problems and implementing a solution, requires an understanding of the complexity of the decision domain to the point where the problem and its complexity, as well as the requirements for supporting decision makers, can be described. Research in the Decision Support Systems domain has been extensive over the last thirty years with an emphasis on the development of further technology and better applications on the one hand, and on the other hand, a social approach focusing on understanding what decision making is about and how developers and users should interact. This research project considers a combined approach that endeavours to understand the thinking behind managers’ decision making, as well as their informational and decisional guidance and decision support requirements. This research utilises a cognitive framework, developed in 1985 by Humphreys and Berkeley that juxtaposes the mental processes and ideas of decision problem definition and problem solution that are developed in tandem through cognitive refinement of the problem, based on the analysis and judgement of the decision maker. The framework facilitates the separation of what is essentially a continuous process, into five distinct levels of abstraction of manager’s thinking, and suggests a structure for the underlying cognitive activities. Alter (2004) argues that decision support provides a richer basis than decision support systems, in both practice and research. The constituent literature on decision support, especially in regard to modern high profile systems, including Business Intelligence and Business analytics, can give the impression that all ‘smart’ organisations utilise decision support and data analytics capabilities for all of their key decision making activities. However this empirical investigation indicates a very different reality.

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The amount and quality of available biomass is a key factor for the sustainable livestock industry and agricultural management related decision making. Globally 31.5% of land cover is grassland while 80% of Ireland’s agricultural land is grassland. In Ireland, grasslands are intensively managed and provide the cheapest feed source for animals. This dissertation presents a detailed state of the art review of satellite remote sensing of grasslands, and the potential application of optical (Moderate–resolution Imaging Spectroradiometer (MODIS)) and radar (TerraSAR-X) time series imagery to estimate the grassland biomass at two study sites (Moorepark and Grange) in the Republic of Ireland using both statistical and state of the art machine learning algorithms. High quality weather data available from the on-site weather station was also used to calculate the Growing Degree Days (GDD) for Grange to determine the impact of ancillary data on biomass estimation. In situ and satellite data covering 12 years for the Moorepark and 6 years for the Grange study sites were used to predict grassland biomass using multiple linear regression, Neuro Fuzzy Inference Systems (ANFIS) models. The results demonstrate that a dense (8-day composite) MODIS image time series, along with high quality in situ data, can be used to retrieve grassland biomass with high performance (R2 = 0:86; p < 0:05, RMSE = 11.07 for Moorepark). The model for Grange was modified to evaluate the synergistic use of vegetation indices derived from remote sensing time series and accumulated GDD information. As GDD is strongly linked to the plant development, or phonological stage, an improvement in biomass estimation would be expected. It was observed that using the ANFIS model the biomass estimation accuracy increased from R2 = 0:76 (p < 0:05) to R2 = 0:81 (p < 0:05) and the root mean square error was reduced by 2.72%. The work on the application of optical remote sensing was further developed using a TerraSAR-X Staring Spotlight mode time series over the Moorepark study site to explore the extent to which very high resolution Synthetic Aperture Radar (SAR) data of interferometrically coherent paddocks can be exploited to retrieve grassland biophysical parameters. After filtering out the non-coherent plots it is demonstrated that interferometric coherence can be used to retrieve grassland biophysical parameters (i. e., height, biomass), and that it is possible to detect changes due to the grass growth, and grazing and mowing events, when the temporal baseline is short (11 days). However, it not possible to automatically uniquely identify the cause of these changes based only on the SAR backscatter and coherence, due to the ambiguity caused by tall grass laid down due to the wind. Overall, the work presented in this dissertation has demonstrated the potential of dense remote sensing and weather data time series to predict grassland biomass using machine-learning algorithms, where high quality ground data were used for training. At present a major limitation for national scale biomass retrieval is the lack of spatial and temporal ground samples, which can be partially resolved by minor modifications in the existing PastureBaseIreland database by adding the location and extent ofeach grassland paddock in the database. As far as remote sensing data requirements are concerned, MODIS is useful for large scale evaluation but due to its coarse resolution it is not possible to detect the variations within the fields and between the fields at the farm scale. However, this issue will be resolved in terms of spatial resolution by the Sentinel-2 mission, and when both satellites (Sentinel-2A and Sentinel-2B) are operational the revisit time will reduce to 5 days, which together with Landsat-8, should enable sufficient cloud-free data for operational biomass estimation at a national scale. The Synthetic Aperture Radar Interferometry (InSAR) approach is feasible if there are enough coherent interferometric pairs available, however this is difficult to achieve due to the temporal decorrelation of the signal. For repeat-pass InSAR over a vegetated area even an 11 days temporal baseline is too large. In order to achieve better coherence a very high resolution is required at the cost of spatial coverage, which limits its scope for use in an operational context at a national scale. Future InSAR missions with pair acquisition in Tandem mode will minimize the temporal decorrelation over vegetation areas for more focused studies. The proposed approach complements the current paradigm of Big Data in Earth Observation, and illustrates the feasibility of integrating data from multiple sources. In future, this framework can be used to build an operational decision support system for retrieval of grassland biophysical parameters based on data from long term planned optical missions (e. g., Landsat, Sentinel) that will ensure the continuity of data acquisition. Similarly, Spanish X-band PAZ and TerraSAR-X2 missions will ensure the continuity of TerraSAR-X and COSMO-SkyMed.

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As economies, societies, and environments change, official statistics evolve and develop to reflect those changes. In reaction to disruptive innovations arising from globalisation, technological advances, and cultural changes, the pace of change of official statistics will accelerate in the future. The motivation for change may also be more existential than that of the past as official statisticians consider the survival of their discipline. This article examines some of the emerging developments and questions whether they present threats or offer opportunities.

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A methodology for improved power controller switching in mobile Body Area Networks operating within the ambient healthcare environment is proposed. The work extends Anti-windup and Bumpless transfer results to provide a solution to the ambulatory networking problem that ensures sufficient biometric data can always be regenerated at the base station. The solution thereby guarantees satisfactory quality of service for healthcare providers. Compensation is provided for the nonlinear hardware constraints that are a typical feature of the type of network under consideration and graceful performance degradation in the face of hardware output power saturation is demonstrated, thus conserving network energy in an optimal fashion.

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The healthcare industry is beginning to appreciate the benefits which can be obtained from using Mobile Health Systems (MHS) at the point-of-care. As a result, healthcare organisations are investing heavily in mobile health initiatives with the expectation that users will employ the system to enhance performance. Despite widespread endorsement and support for the implementation of MHS, empirical evidence surrounding the benefits of MHS remains to be fully established. For MHS to be truly valuable, it is argued that the technological tool be infused within healthcare practitioners work practices and used to its full potential in post-adoptive scenarios. Yet, there is a paucity of research focusing on the infusion of MHS by healthcare practitioners. In order to address this gap in the literature, the objective of this study is to explore the determinants and outcomes of MHS infusion by healthcare practitioners. This research study adopts a post-positivist theory building approach to MHS infusion. Existing literature is utilised to develop a conceptual model by which the research objective is explored. Employing a mixed-method approach, this conceptual model is first advanced through a case study in the UK whereby propositions established from the literature are refined into testable hypotheses. The final phase of this research study involves the collection of empirical data from a Canadian hospital which supports the refined model and its associated hypotheses. The results from both phases of data collection are employed to develop a model of MHS infusion. The study contributes to IS theory and practice by: (1) developing a model with six determinants (Availability, MHS Self-Efficacy, Time-Criticality, Habit, Technology Trust, and Task Behaviour) and individual performance-related outcomes of MHS infusion (Effectiveness, Efficiency, and Learning), (2) examining undocumented determinants and relationships, (3) identifying prerequisite conditions that both healthcare practitioners and organisations can employ to assist with MHS infusion, (4) developing a taxonomy that provides conceptual refinement of IT infusion, and (5) informing healthcare organisations and vendors as to the performance of MHS in post-adoptive scenarios.

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The aging population in many countries brings into focus rising healthcare costs and pressure on conventional healthcare services. Pervasive healthcare has emerged as a viable solution capable of providing a technology-driven approach to alleviate such problems by allowing healthcare to move from the hospital-centred care to self-care, mobile care, and at-home care. The state-of-the-art studies in this field, however, lack a systematic approach for providing comprehensive pervasive healthcare solutions from data collection to data interpretation and from data analysis to data delivery. In this thesis we introduce a Context-aware Real-time Assistant (CARA) architecture that integrates novel approaches with state-of-the-art technology solutions to provide a full-scale pervasive healthcare solution with the emphasis on context awareness to help maintaining the well-being of elderly people. CARA collects information about and around the individual in a home environment, and enables accurately recognition and continuously monitoring activities of daily living. It employs an innovative reasoning engine to provide accurate real-time interpretation of the context and current situation assessment. Being mindful of the use of the system for sensitive personal applications, CARA includes several mechanisms to make the sophisticated intelligent components as transparent and accountable as possible, it also includes a novel cloud-based component for more effective data analysis. To deliver the automated real-time services, CARA supports interactive video and medical sensor based remote consultation. Our proposal has been validated in three application domains that are rich in pervasive contexts and real-time scenarios: (i) Mobile-based Activity Recognition, (ii) Intelligent Healthcare Decision Support Systems and (iii) Home-based Remote Monitoring Systems.

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With the swamping and timeliness of data in the organizational context, the decision maker’s choice of an appropriate decision alternative in a given situation is defied. In particular, operational actors are facing the challenge to meet business-critical decisions in a short time and at high frequency. The construct of Situation Awareness (SA) has been established in cognitive psychology as a valid basis for understanding the behavior and decision making of human beings in complex and dynamic systems. SA gives decision makers the possibility to make informed, time-critical decisions and thereby improve the performance of the respective business process. This research paper leverages SA as starting point for a design science project for Operational Business Intelligence and Analytics systems and suggests a first version of design principles.

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Salutogenesis is now accepted as a part of the contemporary model of disease: an individual is not only affected by pathogenic factors in the environment, but those that promote well-being or salutogenesis. Given that "environment" extends to include the built environment, promotion of salutogenesis has become part of the architectural brief for contemporary healthcare facilities, drawing on an increasing evidence-base. Salutogenesis is inextricably linked with the notion of person-environment "fit". MyRoom is a proposal for an integrated architectural and pervasive computing model, which enhances psychosocial congruence by using real-time data indicative of the individual's physical status to enable the environment of his/her room (colour, light, temperature) to adapt on an on-going basis in response to bio-signals. This work is part of the PRTLI-IV funded programme NEMBES, investigating the use of embedded technologies in the built environment. Different care contexts require variations in the model, and iterative prototyping investigating use in different contexts will progressively lead to the development of a fully-integrated adaptive salutogenic single-room prototype.

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This research assesses the impact of user charges in the context of consumer choice to ascertain how user charges in healthcare impact on patient behaviour in Ireland. Quantitative data is collected from a subset of the population in walk-in Urgent Care Clinics and General Practitioner surgeries to assess their responses to user charges and whether user charges are a viable source of part-funding healthcare in Ireland. Examining the economic theories of Becker (1965) and Grossman (1972), the research has assessed the impact of user charges on patient choice in terms of affordability and accessibility in healthcare. The research examined a number of private, public and part-publicly funded healthcare services in Ireland for which varying levels of user charges exist depending on patients’ healthcare cover. Firstly, the study identifies the factors affecting patient choice of privately funded walk-in Urgent Care Clinics in Ireland given user charges. Secondly, the study assesses patient response to user charges for a mainly public or part-publicly provided service; prescription drugs. Finally, the study examines patients’ attitudes towards the potential application of user charges for both public and private healthcare services when patient choice is part of a time-money trade-off, convenience choice or preference choice. These services are valued in the context of user charges becoming more prevalent in healthcare systems over time. The results indicate that the impact of user charges on healthcare services vary according to socio-economic status. The study shows that user charges can disproportionately affect lower income groups and consequently lead to affordability and accessibility issues. However, when valuing the potential application of user charges for three healthcare services (MRI scans, blood tests and a branded over a generic prescription drug), this research indicates that lower income individuals are willing to pay for healthcare services, albeit at a lower user charge than higher income earners. Consequently, this study suggests that user charges may be a feasible source of part-financing Irish healthcare, once the user charge is determined from the patients’ perspective, taking into account their ability to pay.

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Objectives: The objective of this systematic review was to synthesize the available qualitative evidence on the knowledge, attitudes and beliefs of adult patients, healthcare professionals and carers about oral dosage form modification. Design: A systematic review and synthesis of qualitative studies was undertaken, utilising the thematic synthesis approach. Data sources: The following databases were searched from inception to September 2015: PubMed, Medline (EBSCO), EMBASE, CINAHL, PsycINFO, Web of Science, ProQuest Databases, Scopus, Turning Research Into Practice (TRIP), Cochrane Central Register of Controlled Trials (CENTRAL) and the Cochrane Database of Systematic Reviews (CDSR). Citation tracking and searching the references lists of included studies was also undertaken. Grey literature was searched using the OpenGrey database, internet searching and personal knowledge. An updated search was undertaken in June 2016. Review methods: Studies meeting the following criteria were eligible for inclusion; (i) used qualitative data collection and analysis methods; (ii) full-text was available in English; (iii) included adult patients who require oral dosage forms to be modified to meet their needs or; (iv) carers or healthcare professionals of patients who require oral dosage forms to be modified. Two reviewers independently appraised the quality of the included studies using the Critical Appraisal Skills Programme Checklist. A thematic synthesis was conducted and analytical themes were generated. Results: Of 5455 records screened, seven studies were eligible for inclusion; three involved healthcare professionals and the remaining four studies involved patients. Four analytical themes emerged from the thematic synthesis: (i) patient-centred individuality and variability; (ii) communication; (iii) knowledge and uncertainty and; (iv) complexity. The variability of individual patient’s requirements, poor communication practices and lack of knowledge about oral dosage form modification, when combined with the complex and multi-faceted healthcare environment complicate decision making regarding oral dosage form modification and administration. Conclusions: This systematic review has highlighted the key factors influencing the knowledge, attitudes and beliefs of patients and healthcare professionals about oral dosage form modifications. The findings suggest that in order to optimise oral medicine modification practices the needs of individual patients should be routinely and systematically assessed and decision-making should be supported by evidence based recommendations with multidisciplinary input. Further research is needed to optimise oral dosage form modification practices and the factors identified in this review should be considered in the development of future interventions.