Review of Big Data Tools for HealthCare System with case study on patient database storage methodology (original) (raw)
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Healthcare is the diagnosis, treatment, and prevention of disease, illness, injury, and other physical and mental impairments in human beings. Healthcare is delivered by practitioners in allied health, dentistry, midwifery-obstetrics, medicine, nursing, optometry, pharmacy, psychology and other care providers. In healthcare service to provide better performance to identify some diseases like blood pressure, skin cancer, diabetes etc. with the sensor devices those are connected with home server by using wireless sensor or wired network policies. The size of data sets being collected and analyzed in the industry for healthcare business intelligence is growing rapidly. In healthcare service case we have to concern about massive data services. To store and process huge amount of unstructured data by using traditional database system is so difficult. To alleviate this drawback we can use Hadoop architecture that has functionality to store and process huge amount of unstructured data. Hadoop Distributed file system (HDFS) can store and the Hadoop MapReduce framework process huge amount of unstructured data that enables easy development of scalable parallel applications. This paper’s main motivation is to ensure better data transmission, data reliability and massive data processing with High Availability (HA) based network storage solution. So, in this paper we proposed MapReduce Agent (MRA) to process Big-Data and also proposed iSCSI Protocol adapted Network Attached Storage (NAS) system for healthcare service.
A Study on the Status of Patient Data Handling: Current Scenario and Future Recommendations
IJCSIS Vol 17 No 2, 2019
Abstract— Day by day different types of new diseases are faced by human in different stages of their age. For preventing and reducing these diseases, various health-care system and patient data handling system are introduced with rapid rising of technology. The aim of this research is to find different types of latest technology for handling patient data in an efficient manner. With the increased of medical data various problem are created such as to collect and preserve data for further analysis; give proper treatment for different patient; collect medical data from different places and integrate them into a single framework; give security and manage different databases; provide real time data monitoring system; data transmission through on-line process; management different database; maintaining diabetes, mental pressure, wright measurement, loss of hearing capability. These problems can be solved by using modern technology such as health management system, electronic health record (EHR), cloud based hybrid database system of NoSQL, Internet of things (IoT), on-line transaction processing (OLTP), hadoop and Map Reduce model, big data management framework, data mining, intelligent weight management system (iWMS), CVOTION approach, e-health and telemedicine. The main objectives of this paper is to analyze different modern technology for patient data handling and compare them for finding which technology is more efficient. This research finally recommends the technology with which we can handle patient data more conveniently. Keywords— ehr; health-care; hadoop; nosql; iot; oltp; diabetes; iwms; telemedicine; big data.
The Architectural Design of Healthcare Service with Big-Data Storing Mechanism
Abstract: Healthcare is the diagnosis, treatment, and prevention of disease, illness, injury, and other physical and mental impairments in human beings. Healthcare is delivered by practitioners in allied health, dentistry, midwifery-obstetrics, medicine, nursing, optometry, pharmacy, psychology and other care providers. In healthcare service to provide better performance to identify some diseases like blood pressure, skin cancer, diabetes etc. with the sensor devices those are connected with home server by using wireless sensor or wired network policies. The size of data sets being collected and analyzed in the industry for healthcare business intelligence is growing rapidly. In healthcare service case we have to concern about massive data services. To store and process huge amount of unstructured data by using traditional database system is so difficult. To alleviate this drawback we can use Hadoop architecture that has functionality to store and process huge amount of unstructured data. Hadoop Distributed file system (HDFS) can store and the Hadoop MapReduce framework process huge amount of unstructured data that enables easy development of scalable parallel applications. This paper’s main motivation is to ensure better data transmission, data reliability and massive data processing with High Availability (HA) based network storage solution. So, in this paper we proposed MapReduce Agent (MRA) to process Big-Data and also proposed iSCSI Protocol adapted Network Attached Storage (NAS) system for healthcare service. Keywords: HealthCare Service, Big-Data, Hadoop, Sensor Integration and iSCSI.
A survey of issues and solutions of health data management systems
Innovations in Systems and Software Engineering
In the recent era, data science plays an important role in the health-care domain to provide a cost-effective and better treatment procedure. To achieve this goal, the data management system has a huge contribution by controlling, arranging, storing and preprocessing a large volume of health dataset. Already there are a lot of investigation and designing of different approaches to support the big data applications in different domain. Still, management of big data is a challenging task for the data scientist due to the complex characteristics of data and demands of the application. In this survey paper, we discuss the occurring challenges and it's possible solutions by considering the entities related to data services. It will help the data scientist to understand the supporting parameters of data storage system for designing big data management system.
Intelligent and sustainable approaches for medical big data management
ELSVIER, 2022
Big data, it's a trend in every company whether it is the healthcare or the IT industry. A huge amount of data is generated everywhere which needs to be managed properly. These managed data are important for interpretation and analysis in the future. These will enhance the new computational techniques to work on big "V"s that is volume, velocity, veracity, variety, and value. In healthcare too, lots of data are generated which is stored in electronic form. The evolution of data on daily basis requires proper handling and management. As big data to knowledge is the latest demand (Margolis et al., 2014). The term big data is introduced in 1997 (Cox & Ellsworth, 1997) by John R. Mashey. And is a big challenge in biomedical research. All the big databases like international cancer genome consortium, some disease databases like international rare disease consortium, and the international human epigenome consortium, are the ones that are part of data resources. These analytics require capabilities for the representation and modeling of the health data, optimization of different algorithms, and computational power. In the healthcare industry, five distinctive capabilities of data are required: identification of patterns in data that provides care, analysis of unstructured data, providing decision support, better prediction, and traceability (Wang, Kung, & Byrd, 2018). Data generation and collection are faster than data preprocessing and analysis. To cover this gap there is a need for technological progress in various kinds of data acquisition. All this information generated and fed to computers is of utmost importance whether it is molecular information, phenotypic information of an individual patient, or others. These biomedical data need protection, storage capacity, etc. Hence cloud computing techniques come into existence. The cloud computing (CC) standard has engrossed a lot of attraction from both industry and scholars. It proposes diverse services including asset pooling, multi-tenancy, and flexibility (Chkirbene et al., 2020). While the cloud computing standard raises economic efficiency, security is one of the important concerns in adopting the cloud computing model (Chkirbene, Erbad, & Hamila, 2019). Cloud computing is a new operating technology that advanced the information technology (IT) industry, it is an expansion of equivalent computing, distributed computing, and grid computing over the same or different networks, (Sniezynski, Nawrocki, & Wilk, 2019). Cloud computing technology is the combination and development of virtualization, utility computing, and all the
Survey paper on Health Care Big Data Analysis
2017
In this world more number facing the problem of blast data and size of data base used in industry has been growing more rates. Many sources are generating data through web media, social media data, sensor data, connected devices (Smart phone) and click stream data. More amount of data analyzing, processing as well as extracting information is big challenging task. Big data is a set of huge amount data such data can't be processed or analyze by using conventional computing technique. Difficulties are included like transfer, storage data, analytics, search, support huge amount of data to database and sharing. Health care needs to be updated with new period of big data this added health care data analysed. Existing database system like Oracle, MySQL, Postgre SQL and RDBMS are used. If you want large dataset size, read/write concurrency or both you will find that the services of an RDBMS come at a vast performance sentence and make division naturally complex. To deal with bulk /larg...
A Comprehensive Health Electronic Record System with MySQL RDMS, QGIS Database and MongoDB
SSRN Electronic Journal
The advancement in data storage systems and novel data types has made organizations to stop relying on the use of simple client/server I.T infrastructure and leverage more on multiple categories of database systems to keep heterogeneous data. The study exploits the benefits around deploying hybrid relational database management systems and NoSQL systems while developing better electronic health records (EHR) systems within health facilities alongside facility decision support system (FDSS). More particularly, GIS, MySQL and Mongo DB databases were integrated to enhance EHR systems alongside offering improve clinical decision support. The study adopted experimental design to develop the Electronic Health Records System using GIS, MySQL and Mongo DB software to create the database. Findings revealed that the atomicity, consistency, isolation and durability feature typical of relational database management systems guaranteed data security, integrity, ease of access and efficient transaction processing. Mongo database offered the system a more precise internal data structure and solid scalability along with simplified mapping of application objects to the underlying database design. The GIS database enabled a clear visualization of patients' geographical locations, medical facilities, and the physical location of the physicians. Integrating these database systems within the health care arena was instrumental in compelling application systems to adhere to the HIPAA EHR standards without compromising performance and scalability.
SURVEY OF HEALTH CARE INDUSTRIES BY STUDY THE ARCHITECTURE AND FUNCTIONALITY OF BIG DATA ANALYTICS
The paper advanced here intended to impart cognizance regarding big data analysis and its influence in every expanse such as hospitals, educational organisations, government offices etc. Veracious and scrupulous anatomization of huge volume of data collected from multifarious provenance is essential, as contemporarily real time scrutinization of information & facts using Hadoop and other supportive languages like pig, hiveetc., is playing an efficacious job in decision making for various organizations. The aspiration and insight of the paper is to recount big data use cases for exigency situations in hospitals and health care centres. Big data refers to tremendously & exceedingly huge magnitude of data that has to be analysed and studied computationally or electronically for generating specific and unerring result. The term big data refers to gigantic quantity of data both ordered and unordered strenuous and laborious to compute via orthodox conventional techniques. Apt analysis and examination of bulk of data helps an organisation to make smart decisions. Presently a lot many health care organizations has not clutched the ease and advantage of wangling data analytics. The prudent innuendo and judicious implementation of big data analytics for health care industry is of significant importance. This paper study architecture, analytics, developments and functionalities of big data for its tactical enactment in health care industry. According to the content and results of numerous bid data analytics cases many capabilities were pinpointed. In this paper distinct strategy, functionalities, findings, benefits and capabilities are hatched for potent data analytics. Bulk of data is originating every minute from different departments of a health care industry. Today it is obligatory to digitalize this bulk of data for the sake of cost optimisation, improved quality and service. It is mandatory to examine this massive data to evaluate new situations and to stand on new circumstances. This data analysis helps in exploring and generating new results according to the situation by recognizing different patterns, their relationships using machine learning algorithms. The paper advances information about data generated by different systems, states of data and all the security issues in handling and analysis the data. The massive flood of data & information originated at faster and higher varieties and velocities in health protection organisations adds more complexity to their work load. Poor healthcare information management and incomplete & inadequate assembling of hospital management systems are severely damaging and hampering the efforts. These prevailing circumstancesin industry are needlessly and excessively escalating the costs & expenditure for patients and service providing organizations. There is a genuine entailing of IT/CS aspirants for data digitalization, expanding performance region, excelling patient experience and enhancing service quality, IT artifacts are required in hospitals to data driving traditional system and to evolve the existing traditional governing system to an intelligent decision support system which succour and aids the organisation to examine massive volume, variety and velocity of data. Recent studies and evidences reveal that hardly 44% of health industries are embracing meticulous study & significant analysis to support smart decision making process. Big data analytics circumscribe numerous analytical mechanisms and tricks best fitted for analysing clinics unordered massive data. This huge volume data to be analysed is stored at NoSQL and Apache HBase systems and its management is done via Marklogic, Apache Cassandrae, and MongoDB for data updation, retrieval and integration etc... The utilisation of this management system even provides the ability to transfer data from traditional to new OS. This enactment of this
A Case for the Adoption of an In-Memory Based Technique for Healthcare Big Data Management
International Journal of Computer (IJC), 2017
In healthcare organizations, the amount of data that are generated daily are on the increase with every visit by patient. The generated data through vital signs' readings such as body temperature, pulse rate, respiratory rate, blood pressure, body weight among others are now accumulated into big data. Recently, the growth of data is averaged at about 35 percent annually. The implication is that the amount of storage needed to hold the data doubles within a period of three years. No doubt, if these data are processed and analyzed properly, it holds immense value in diagnosis and predictive medical conditions. However, the ever increasing volume of data has brought with it some big challenges. One of such is how healthcare organizations are going to store and access the vast amount of inherent information. In this paper, we discussed the need for storing medical Big Data in the main memory (In-Memory) as a way of addressing storage and access to information challenges of big data in health care delivery system. With current trends in technology advancement, there is an availability of storage systems with increased memory capacities. The storage of data in main memory can achieve a performance improvement of up to a factor of 100,000 or more. With this achievable performance, In-Memory Data Management proves to be a viable option.
Implementation of Big Data in Health Information Systems: Sample Approaches in Saudi Hospital
International Journal of Computer Applications
Big data concept provides opportunity to exchange patient's medical information to the different healthcare providers. Health Information System (HIS) has created the ability to electronically store, maintain and move data across the world in a matter of seconds and has the potential to provide healthcare with tremendous increasing productivity and quality of services. Big data analytics is a growth area with the potential to provide useful insight in health information system. Big Data can unify all patient related data to get more option to view patient records to analyze and predict early disease detection. Big data supports and improve clinical practices, new drug development and health care financing process. Implementation of Health Information system (HIS) continues to expand infrastructure in Medical field due to enormous number of patient comes across to store medical data. In this paper we focus the Big data concept to increase and store patients details in Saudi public hospitals with maximum utilization. Most of the Saudi public and private hospitals Health information system locally connected and maintained by own hospital admin. There is no system implemented to share the patient health record, treatment details and medical prescription data to other hospital. The main problem in the Saudi hospital, Health information is not centralized due to unstructured, semi structured data maintain by the Saudi hospital. Proper Health information system is able to offer correct and complete personal health and medical summary through the Big data methods. This paper introduces the Big Data concept and characteristics, health care data and some major issues of Big Data. Big Data methods and challenges in medical applications and health information system are also discussed in this study. This study provides a base model to increase the use of big data in health information system and can assist to understand the breadth of big data applications.