Intelligent Techniques for Analysis of Big Data About Healthcare and Medical Records

2022 ◽  
pp. 431-454
Author(s):  
Pinar Kirci
Keyword(s):  
Operating System ◽  
Big Data ◽  
Electronic Medical Records ◽  
Data Storage ◽  
Medical Records ◽  
Data Access ◽  
Efficient Solutions ◽  
Unstructured Data ◽  
Distributed File System ◽  
Healthcare Industry

To define huge datasets, the term of big data is used. The considered “4 V” datasets imply volume, variety, velocity and value for many areas especially in medical images, electronic medical records (EMR) and biometrics data. To process and manage such datasets at storage, analysis and visualization states are challenging processes. Recent improvements in communication and transmission technologies provide efficient solutions. Big data solutions should be multithreaded and data access approaches should be tailored to big amounts of semi-structured/unstructured data. Software programming frameworks with a distributed file system (DFS) that owns more units compared with the disk blocks in an operating system to multithread computing task are utilized to cope with these difficulties. Huge datasets in data storage and analysis of healthcare industry need new solutions because old fashioned and traditional analytic tools become useless.

Intelligent Techniques for Analysis of Big Data About Healthcare and Medical Records

2018 ◽  
pp. 559-582 ◽  
Author(s):  
Pinar Kirci
Keyword(s):  
Operating System ◽  
Big Data ◽  
Electronic Medical Records ◽  
Data Storage ◽  
Medical Records ◽  
Data Access ◽  
Efficient Solutions ◽  
Unstructured Data ◽  
Distributed File System ◽  
Healthcare Industry

To define huge datasets, the term of big data is used. The considered “4 V” datasets imply volume, variety, velocity and value for many areas especially in medical images, electronic medical records (EMR) and biometrics data. To process and manage such datasets at storage, analysis and visualization states are challenging processes. Recent improvements in communication and transmission technologies provide efficient solutions. Big data solutions should be multithreaded and data access approaches should be tailored to big amounts of semi-structured/unstructured data. Software programming frameworks with a distributed file system (DFS) that owns more units compared with the disk blocks in an operating system to multithread computing task are utilized to cope with these difficulties. Huge datasets in data storage and analysis of healthcare industry need new solutions because old fashioned and traditional analytic tools become useless.

scholarly journals Prevalence of chronic disease in older adults in multitier eye-care facilities in South India: Electronic medical records-driven big data analytics report

2021 ◽  
Vol 69 (12) ◽  
pp. 3618
Author(s):  
UmeshChandra Behera ◽  
Brooke Salzman ◽  
AnthonyVipin Das ◽  
GumpiliSai Prashanthi ◽  
Parth Lalakia ◽  
...  
Keyword(s):  
Older Adults ◽  
Big Data ◽  
Chronic Disease ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Data Analytics ◽  
South India ◽  
Big Data Analytics ◽  
Eye Care ◽  
Care Facilities

Storage Infrastructure for Big Data and Cloud

2014 ◽  
pp. 110-128 ◽  
Author(s):  
Anupama C. Raman
Keyword(s):  
Big Data ◽  
Real Time ◽  
Data Storage ◽  
Unstructured Data ◽  
Storage Area Networks ◽  
Storage Technology ◽  
Object Based ◽  
Storage Area ◽  
Storage Technologies ◽  
Big Data Storage

Unstructured data is growing exponentially. Present day storage infrastructures like Storage Area Networks and Network Attached Storage are not very suitable for storing huge volumes of unstructured data. This has led to the development of new types of storage technologies like object-based storage. Huge amounts of both structured and unstructured data that needs to be made available in real time for analytical insights is referred to as Big Data. On account of the distinct nature of big data, the storage infrastructures for storing big data should possess some specific features. In this chapter, the authors examine the various storage technology options that are available nowadays and their suitability for storing big data. This chapter also provides a bird's eye view of cloud storage technology, which is used widely for big data storage.

scholarly journals Completeness and accuracy of anthropometric measurements in electronic medical records for children attending primary care

2018 ◽  
Vol 25 (1) ◽  
pp. 19-26 ◽  
Author(s):  
Sarah Carsley ◽  
Catherine S. Birken ◽  
Patricia C. Parkin ◽  
Eleanor Pullenayegum ◽  
Karen Tu
Keyword(s):  
Primary Care ◽  
Body Mass Index ◽  
Electronic Medical Records ◽  
Body Mass ◽  
Medical Records ◽  
Data Access ◽  
World Health ◽  
Z Score ◽  
Data Completeness ◽  
Weight Data

BackgroundElectronic medical records (EMRs) from primary care may be a feasible source of height and weight data. However, the use of EMRs in research has been impeded by lack of standardisation of EMRs systems, data access and concerns about the quality of the data.ObjectivesThe study objectives were to determine the data completeness and accuracy of child heights and weights collected in primary care EMRs, and to identify factors associated with these data quality attributes.MethodsA cross-sectional study examining height and weight data for children <19 years from EMRs through the Electronic Medical Record Administrative data Linked Database (EMRALD), a network of family practices across the province of Ontario. Body mass index z-scores were calculated using the World Health Organization Growth Standards and Reference.ResultsA total of 54,964 children were identified from EMRALD. Overall, 93% had at least one complete set of growth measurements to calculate a body mass index (BMI) z-score. 66.2% of all primary care visits had complete BMI z-score data. After stratifying by visit type 89.9% of well-child visits and 33.9% of sick visits had complete BMI z-score data; incomplete BMI z-score was mainly due to missing height measurements. Only 2.7% of BMI z-score data were excluded due to implausible values.ConclusionsData completeness at well-child visits and overall data accuracy were greater than 90%. EMRs may be a valid source of data to provide estimates of obesity in children who attend primary care.

scholarly journals Hybrid blockchain–based privacy-preserving electronic medical records sharing scheme across medical information control system

2020 ◽  
Vol 53 (7-8) ◽  
pp. 1286-1299
Author(s):  
Yu Cao ◽  
Yi Sun ◽  
Jiangsong Min
Keyword(s):  
Big Data ◽  
Control System ◽  
Control Systems ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Medical Information ◽  
Information Control ◽  
Personal Privacy ◽  
Sharing Scheme ◽  
Medical Big Data

With the development of big data and medical information control system, electronic medical records sharing across organizations for better medical treatment and advancement has attracted much attention both from academic and industrial areas. However, the source of big data, personal privacy concern, inherent trust issues across organizations and complicated regulation hinder the great progress of healthcare intelligence. Blockchain, as a novel technique, has been used widely to resolve the privacy and security issues in electronic medical records sharing process. In this paper, we propose a hybrid blockchain–based electronic medical records sharing scheme to address the privacy and trust issues across the medical information control systems, rendering the electronic medical records sharing process secure, effective, relatively transparent, immutable, traceable and auditable. Considering the above confidential issues, we use different sharing methods for different parts of medical big data. We share privacy-sensitive couples on the consortium blockchain, while sharing the non-sensitive parts on the public blockchain. In this way, authorized medical information control systems within the consortium can access the data on it for precise medical diagnosis. Institutions such as universities and research institutes can get access to the non-sensitive parts of medical big data for scientific research on symptoms to evolve medical technologies. A working prototype is implemented to demonstrate how the hybrid blockchain facilitates the pharmaceutical operations in a healthcare information control ecosystem. A blockchain benchmark tool Hyperledger Caliper is used to evaluate the performance of hybrid blockchain–based electronic medical records sharing scheme on throughput and average latency which proves to be practicable and excellent.

scholarly journals Toward a Literature-Driven Definition of Big Data in Healthcare

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Emilie Baro ◽  
Samuel Degoul ◽  
Régis Beuscart ◽  
Emmanuel Chazard
Keyword(s):  
Big Data ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Data Reuse ◽  
Secondary Use ◽  
Meaningful Information ◽  
Fields Of Study ◽  
Study Characteristics ◽  
Definition Of ◽  
Privacy Issues

Objective.The aim of this study was to provide a definition of big data in healthcare.Methods.A systematic search of PubMed literature published until May 9, 2014, was conducted. We noted the number of statistical individuals(n)and the number of variables(p)for all papers describing a dataset. These papers were classified into fields of study. Characteristics attributed to big data by authors were also considered. Based on this analysis, a definition of big data was proposed.Results.A total of 196 papers were included. Big data can be defined as datasets withLog⁡(n*p)≥7. Properties of big data are its great variety and high velocity. Big data raises challenges on veracity, on all aspects of the workflow, on extracting meaningful information, and on sharing information. Big data requires new computational methods that optimize data management. Related concepts are data reuse, false knowledge discovery, and privacy issues.Conclusion.Big data is defined by volume. Big data should not be confused with data reuse: data can be big without being reused for another purpose, for example, in omics. Inversely, data can be reused without being necessarily big, for example, secondary use of Electronic Medical Records (EMR) data.

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