Applied Medical Informatics (original) (raw)
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Applied Medical Informatics, 2020
For a long time, High Performance Computing (HPC) has been critical for running large-scale modeling and simulation using numerical models. The big data analytics domain (BDA) has been rapidly developed over the last years to process huge amounts of data now being generated in various domains. But, in general, the data analytics software was not developed inside the scientific computing community, and new approaches were adopted by BDA specialists. Data-intensive applications are needed in various fields of medicine and healthcare ranges from advanced research— as genomics, proteomics, epidemiology and systems biology—to medical diagnosis and treatments, or to commercial initiatives to develop new drugs. BDA needs the infrastructure and the fundamentals of HPC in order to face with the needed computational challenges. There are important differences in the approaches of these two domains: those that are working in BDA focus on the 5Vs of big data which are: volume, velocity, variety...
High performance computing in biomedical informatics
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The last few years have witnessed significant developments in various aspects of Biomedical Informatics, including Bioinformatics, Medical Informatics, Public Health Informatics, and Biomedical Imaging. The explosion of medical and biological data requires an associated increase in the scale and sophistication of the automated systems and intelligent tools to enable the researchers to take full advantage of the available databases. The availability of vast amount of biological data continues to represent unlimited opportunities as well as great challenges in biomedical research. Developing innovative data mining techniques and clever parallel computational methods to implement them will surely play an important role in efficiently extracting useful knowledge from the raw data currently available. The proper integration of carefully selected/developed algorithms along with efficient utilization of high performance computing systems form the key ingredients in the process of reaching ...
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Big data analytics in healthcare is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. The paper describes the nascent field of big data analytics in healthcare, discusses the benefits, outlines an architectural framework and methodology, describes examples reported in the literature, briefly discusses the challenges, and offers conclusions.
Big Data: A Challenging Opportunity for Biomedical Informatics
Amity Journal of Computational Sciences (AJCS) , 2019
In the current technological world big data technologies are being used in bio-informatics research and healthcare. The huge amount of clinical data have been generated and collected at an unoccupied speed and scale. For example, the number of sequencing technologies in the new era producing the trillions of DNA sequence data per day, and the different applications of EHRs-Electronic health records are specifying huge amount of patient data. The amount of processing and analyzing healthcare data is about to decrease dramatically with the help of available technologies. The Big data applications provide new opportunities to enhance new knowledge and establish different type methods to improve the quality of existing healthcare system. The objective of the paper is to evaluate the applications of analytics of Big Data in the biomedicine and healthcare field and the associated outcomes.
Evaluation of Big Data Analytics in Medical Science
International journal of engineering and advanced technology, 2019
In medical science the concept of big data is very important because in the diseases prevention outcome prediction of co-morbidities, mortality and it save the cost of medical treatment it can used. In the evolution of healthcare research and practices the continuously growing field of analytics of big data has play a pivotal role. To analyze, accumulate, assimilate and manage huge volume of structured, disparate and unstructured data that produced by recent system of healthcare, it provide the tools. To inform providers about most effective and efficient treatment pathways and to revamp the health care delivery process the big data of healthcare has the potential. Both insurers and health care providers are incenting by Value-based purchasing programs. To estimate the efficiency and quality of care to find the new ways to leverage health care data defined the insures. During routine health care in data collection current advances in the form of EHR (Electronic Health Records), for clinical application in biological discoveries medical device data have created major opportunities.
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The emergence of massive datasets in a clinical setting presents both challenges and opportunities in data storage and analysis. This so called "big data" challenges traditional analytic tools and will increasingly require novel solutions adapted from other fields. Advances in information and communication technology present the most viable solutions to big data analysis in terms of efficiency and scalability. It is vital those big data solutions are multithreaded and that data access approaches be precisely tailored to large volumes of semi-structured/unstructured data. THE MAPREDUCE PROGRAMMING FRAMEWORK USES TWO TASKS COMMON IN FUNCTIONAL PROGRAMMING: Map and Reduce. MapReduce is a new parallel processing framework and Hadoop is its open-source implementation on a single computing node or on clusters. Compared with existing parallel processing paradigms (e.g. grid computing and graphical processing unit (GPU)), MapReduce and Hadoop have two advantages: 1) fault-tolerant...
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IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), 2014
ABSTRACT We propose a comprehensive information processing, knowledge discovery and simulation platform for Big Data Healthcare. In addition, we present a related, well-defined workflow that promotes model-guided personalized medicine. We start by identifying disease signatures and homogeneous patient groups, whilst modeling case-based patient similarity. Then we analyze correlations between variables and patient groups to deliver accurate and reusable predictive statistical simulation models. Such a framework provides significant advantages on both the clinician's daily routine and targeted biomedical research.
Landscape of Big Medical Data: A Pragmatic Survey on Prioritized Tasks
IEEE Access, 2019
Big medical data poses great challenges to life scientists, clinicians, computer scientists, and engineers. In this paper, a group of life scientists, clinicians, computer scientists and engineers sit together to discuss several fundamental issues. First, what are the unique characteristics of big medical data different from those of the other domains? Second, what are the prioritized tasks in clinician research and practices utilizing big medical data? And do we have enough publicly available data sets for performing those tasks? Third, do the state-of-the-practice and state-of-the-art algorithms perform good jobs? Fourth, are there any benchmarks for measuring algorithms and systems for big medical data? Fifth, what are the performance gaps of state-of-the-practice and state-of-the-art systems handling big medical data currently or in future? Finally but not least, are we, life scientists, clinicians, computer scientists and engineers, ready for working together? We believe answering the above issues will help define and shape the landscape of big medical data.
Big Data Science and Its Applications in Biomedical Research and Healthcare: A Review
There is a consensus among scientists that the analysis of Big Data in health care (such as electronic health records, patient reported outcomes or in motion data) can improve clinical research and the quality of care provided to patients. Big Data mainly deals with the storage and processing of large scale and complex structure data sets for which the traditional methods prove to be incapable. They show a slow responsiveness and lack of scalability, performance and accuracy. The paper provides a systematic review of recent progress and advances in Big Data science, healthcare and the human genome research one of the most promising medical and the healthcare domains. Further the paper includes some of the major challenges and opportunities with a focus on the upcoming and promising areas of medical research: image, signal, and genomics-based analytics to improve big data applications in healthcare in the summarized form.