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- Title
Healthcare Big Data Analytics (BDA): Tools, Techniques, Models, and Applications.
- Authors
Alipour, Jahanpour
- Abstract
Background: Healthcare globally has shifted from a disease-centered approach to a patient-centered approach. Effective management and healthcare BDA are requirements for this shift in approach. Objective: This study aimed to identify the common analytical tools, techniques, and models used for healthcare BDA and its applications. Material and Methods: A literature review was applied using the Scopus, PubMed, Science direct databases, and google scholar search engine over the period 2010-2022 in accordance with the PRISMA statement guideline. The following search terms were used in our search strategy "Big Data" OR "Healthcare Big Data" AND Management OR Process OR Analytic AND Applications OR Uses. Thematic analysis was applied for data extraction. Results: Hadoop and MapReduce are the most common tools, modeling, machine learning, data mining, and visualization approaches are the most common techniques, and Support Vector Machines, Artificial Neural Networks, Random Forest, and Logistic Regression are the most common models for healthcare BDA. Identifying the causes of diseases, predictive analytics to prevent diseases and their outbreaks, monitoring patients' health, selecting treatment alternatives, extracting patterns of diseases, and reducing healthcare costs through the use of clinical insights in care and especially personalized medicine are applications of BDA in healthcare. Conclusion: The main focus of healthcare BDA should be on using the right tools, techniques, and models at the right time to provide insight into clinical data for clinical decision support, evidence-based medicine, personalized medicine, public health, epidemiological purposes, and effective management of healthcare organizations.
- Subjects
CLINICAL decision support systems; BIG data; LITERATURE reviews; SUPPORT vector machines; MEDICAL care costs; ARTIFICIAL neural networks
- Publication
Journal of Biomedical Physics & Engineering, 2023, Vol 13, p256
- ISSN
2251-7200
- Publication type
Article