Ragg, A. and Sudhakaran, P. (2020) Big data in digital healthcare: lessons learnt and recommendations for general practice, Heredity, 124, pages525–534 (2020)https://www.nature.com/articles/s41437-020-0303-2
This article provides invaluable insights into the pivotal role that Big Data is poised to play in the future of technological advancements, particularly in the realm of healthcare. By acknowledging both the vast potential and the existing impediments to its application, the article offers a comprehensive perspective on the complex landscape of Big Data utilization in healthcare. I found the discussion on fragmentation, cost challenges, and data ownership issues particularly enlightening, as it underscores the multifaceted nature of the challenges that must be addressed for effective implementation. This article provides a comprehensive examination of Big Data's role in healthcare, focusing on the oncology field as a case study. It highlights the significant strides made by initiatives like TCGA and the Cancer Moon Shot and offers a comparative analysis of different nations' approaches to data regulation. The proposal for global guidelines, including the creation of a universal patient ID, addresses the complexities of data integration. Additionally, the article emphasizes potential pitfalls such as the lack of diversity in research and security risks associated with machine learning algorithms. Overall, it broadens understanding of opportunities and challenges in integrating Big Data in healthcare, offering forward-thinking recommendations for further exploration in this critical area.
Custers, B. (2016). Big data and data reuse: a taxonomy of data reuse for balancing big data benefits and personal data protection. Oxford, 6(1), 4-15https://www.proquest.com/pqrl/docview/1793652119/5614B6C0C28D419APQ/4?accountid=14541&sourcetype=Scholarly%20Journals
This article presents a comprehensive analysis of the challenges and opportunities surrounding the reuse of data in the context of Big Data, particularly focusing on the legal framework governing personal data protection. It elucidates how Big Data, characterized by vast amounts of often real-time data, holds immense potential for uncovering novel trends and patterns but is hindered by practical, technological, and legal barriers. A significant aspect of the article is its focus on the legal perspective, particularly the implications of existing personal data protection requirements on Big Data initiatives. By providing a taxonomy of data reuse and proposing differentiated approaches based on the proximity to data subjects' awareness and intentions, the article offers insights into reconciling data reuse with privacy concerns. Overall, this article has been immensely beneficial in broadening my understanding of the complexities surrounding data reuse in the era of Big Data. Its nuanced examination of legal implications, coupled with practical insights and recommendations, provides a solid foundation for further exploration and discussion in this critical area of research and practice.
Ramy, E. (2023). Studying the Security and Privacy Issues of Big Data in the Saudi Medical Sector. West Yorkshire, 14(11)https://www.proquest.com/pqrl/docview/2906871317/7C486D5D43214299PQ/6?accountid=14541&sourcetype=Scholarly%20Journals
The comprehensive approach outlined in the paper underscores the necessity for healthcare organizations to adopt proactive measures to mitigate security risks, including encryption, access control, network security, and employee training. By emphasizing the importance of data encryption as a highly effective security measure, the paper highlights the need for identifying sensitive data, selecting appropriate encryption algorithms, and securely managing encryption keys. Furthermore, the paper advocates for a multi-layered approach to data security, incorporating encryption alongside access controls and network security measures to protect against cyberattacks and data breaches. Additionally, the emphasis on employee training underscores the importance of raising awareness among staff members to ensure compliance with security protocols and procedures.In summary, this paper offers practical recommendations and solutions to address the security and privacy concerns associated with the adoption of big data technologies in healthcare. Its emphasis on encryption techniques, access controls, network security, and employee training provides a comprehensive framework for healthcare organizations to enhance data security and protect sensitive patient information effectively.
Puri, G. (2023). Implementation of Big Data Privacy Preservation Technique for Electronic Health Records in Multivendor Environment. West Yorkshire, 14(2)https://www.proquest.com/pqrl/docview/2791786117/7C486D5D43214299PQ/14?accountid=14541&sourcetype=Scholarly%20Journals
The paper acknowledges the uncertainties and challenges surrounding data accessibility, privacy, and security, particularly when sharing sensitive health information with third-party entities. Data privacy and patient confidentiality become critical issues that must be effectively addressed. In addition, this article discusses information disparity and diversity in clinical records, emphasizing the need for unified data representation to facilitate effective data processing and integration. It recommends leveraging modern methods such as big data analytics to convert digital evidence into standardized and coded formats, ensuring appropriate anonymization procedures to protect patient privacy. In summary, this paper provides an in-depth exploration of the complexities of processing health data for secondary applications and proposes a novel integrated architecture for collecting and transforming heterogeneous clinical data while addressing privacy, security, and data representation challenges.
Palanisamy, V. (2022). Towards computational solutions for precision medicine based big data healthcare system using deep learning models: A review. Oxford, 149https://www.proquest.com/pqrl/docview/2715235647/D4B24A168F54FEAPQ/2?accountid=14541&sourcetype=Scholarly%20Journals
The significance of precision medicine lies in its ability to offer tailored treatments based on an individual's unique biology, thereby improving therapeutic outcomes and minimizing adverse effects. The paper discusses the integration of disparate big data sources, including clinical, molecular, behavioral, and environmental data, to develop personalized diagnostic biomarkers and disease-specific drugs and devices. Furthermore, the paper explores the role of deep learning, a sub-domain of machine learning, in advancing precision medicine research. Deep learning models, such as convolutional neural networks and recurrent neural networks, are highlighted for their ability to process complex data types and identify desirable features with improved accuracy. These models play a crucial role in gene regulatory target discovery, disease diagnosis, and drug development, contributing to the advancement of precision medicine. The initiatives taken by several developed countries, such as the Precision Medicine Initiative (PMI) in the USA and investments in diagnostic tools and treatments based on precision medicine in the UK, underscore the global recognition of precision medicine's potential to revolutionize healthcare. In summary, this paper presents a forward-thinking perspective on precision medicine, emphasizing the integration of big data analytics and deep learning models to drive advancements in personalized healthcare services.
Salazar-Reyna, R. (2022). A systematic literature review of data science, data analytics and machine learning applied to healthcare engineering systems. London, 60(2)https://www.proquest.com/pqrl/docview/2624043298/D4B24A168F54FEAPQ/13?accountid=14541&sourcetype=Scholarly%20Journals
This paper offers a comprehensive examination of data science's role in healthcare systems, aiming to assess its impact, benefits, challenges, and trends. Through a systematic literature review, it synthesizes findings from relevant studies, analyzing publication, author, and content characteristics. By defining data science principles and visualizing its components, the paper highlights its potential to improve decision-making and care quality while addressing the inefficiencies in the healthcare industry. This synthesis provides valuable insights into the application of data science, guiding future research and strategic initiatives aimed at leveraging data-driven approaches for better healthcare outcomes. Overall, the paper enhances my understanding of data science's significance in healthcare and its potential to drive innovation and improve patient care.
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Kornelia, B. and Andrzej, S. (Jan 6, 2022) The use of Big Data Analytics in healthcare, 2022; 9(1): 3.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8733917/
This article provides a comprehensive analysis of the potential applications of Big Data Analytics (BDA) in healthcare, drawing from both literature review and direct research conducted on a sample of 217 medical facilities in Poland. The findings reveal a promising shift towards data-driven healthcare practices, with medical facilities leveraging structured and unstructured data for administrative, business, and clinical purposes. The research highlights various sources of data, including databases, transaction records, device and sensor data, as well as unstructured content from emails and documents. Notably, while the utilization of social media data remains relatively low, there's a clear trend towards integrating analytics into clinical decision-making processes. Overall, the study underscores the significant benefits of embracing BDA in healthcare, emphasizing its potential to revolutionize patient treatment and health management practices.
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Tiranee, A. (2018) Using Big Data to Improve Healthcare Serviceshttps://www.youtube.com/watch?v=7t75CNC34vU
Throughout her talk, Dr. Achalakul emphasized the critical role of big data in both the private and public sectors, highlighting her involvement in numerous data analytics and software development projects. Her contributions to advisory boards and committees further underscore her commitment to advancing the field on a national scale. As Assistant President in Innovation and Partnership at King Mongkut University of Technology Thonburi, and Director of the Big Data Experience Center and KMUTT student incubator, Dr. Achalakul has demonstrated a dedication to fostering innovation and nurturing young talents in the realm of big data. Overall, Dr. Tiranee Achalakul's TEDx presentation serves as a catalyst for embracing the potential of big data, showcasing its ability to drive innovation, inform decision-making, and shape the future of technology and society.
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