big data analytics in healthcare industry ppt

Motivation • – • – world's technological per-capita capacity to The store information doubled every 40 months of 2012, 2.5 exabytes (2.5As ×1018) of data/day lational database management systems and Re desktop statistics and visualization packages often have difficulty handling big data. Furthermore, given the nature of traditional databases integrating data of different types such as streaming waveforms and static EHR data is not feasible. The opportunity of addressing the grand challenge requires close cooperation among experimentalists, computational scientists, and clinicians. The healthcare sector has access to huge amounts of data but has been plagued by failures in utilizing the data to curb the cost of rising healthcare and by inefficient systems that stifle faster and better healthcare benefits across the board. Moreover, it is utilized for organ delineation, identifying tumors in lungs, spinal deformity diagnosis, artery stenosis detection, aneurysm detection, and so forth. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Medical image analysis covers many areas such as image acquisition, formation/reconstruction, enhancement, transmission, and compression. As complex physiological monitoring devices are getting smaller, cheaper, and more portable, personal monitoring devices are being used outside of clinical environments by both patients and enthusiasts alike. These techniques are among a few techniques that have been either designed as prototypes or developed with limited applications. This is due to the number of global states rising exponentially in the number of entities [135]. According to this study simultaneous evaluation of all the available imaging techniques is an unmet need. Relief may be on the way. Summary of popular methods and toolkits with their applications. The integration of computer analysis with appropriate care has potential to help clinicians improve diagnostic accuracy [29]. Even if the option to store this data were available, the length of these data captures was typically short and downloaded only using proprietary software and data formats provided by the device manufacturers. Streaming data analytics in healthcare can be defined as a systematic use of continuous waveform (signal varying against time) and related medical record information developed through applied analytical disciplines (e.g., statistical, quantitative, contextual, cognitive, and predictive) to drive decision making for patient care. Therefore, there is a need to develop improved and more comprehensive approaches towards studying interactions and correlations among multimodal clinical time series data. A variety of signal processing mechanisms can be utilized to extract a multitude of target features which are then consumed by a pretrained machine learning model to produce an actionable insight. Utilizing such high density data for exploration, discovery, and clinical translation demands novel big data approaches and analytics. Medical imaging encompasses a wide spectrum of different image acquisition methodologies typically utilized for a variety of clinical applications. Beard contributed to and supervised the whole paper. K. Shackelford, “System & method for delineation and quantification of fluid accumulation in efast trauma ultrasound images,” US Patent Application, 14/167,448, 2014. healthcare trends, it is Big Data - the huge gaps faced by the industry in converting unstructured information bytes into meaningful business intelligence. The cost to sequence the human genome (encompassing 30,000 to 35,000 genes) is rapidly decreasing with the development of high-throughput sequencing technology [16, 17]. Big data in the healthcare industry Increasingly used data-driven care protocols will change healthcare delivery systems globally. Stage 2 of meaningful use requires … Whether from accelerating drug discovery or better understanding patient … Hsu, “Segmentation-based compression: new frontiers of telemedicine in telecommunication,”, F. P. M. Oliveira and J. M. R. S. Tavares, “Medical image registration: a review,”, L. Qu, F. Long, and H. Peng, “3D registration of biological images and models: registration of microscopic images and its uses in segmentation and annotation,”, M. Ulutas, G. Ulutas, and V. V. Nabiyev, “Medical image security and EPR hiding using shamir's secret sharing scheme,”, H. Satoh, N. Niki, K. Eguchi et al., “Teleradiology network system on cloud using the web medical image conference system with a new information security solution,” in, C. K. Tan, J. C. Ng, X. Xu, C. L. Poh, Y. L. Guan, and K. Sheah, “Security protection of DICOM medical images using dual-layer reversible watermarking with tamper detection capability,”. Typically each health system has its own custom relational database schemas and data models which inhibit interoperability of healthcare data for multi-institutional data sharing or research studies. The amount of data is growing exponentially like storing electronic health records of patients (eg.

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