Big data analytics for personalized medicine and healthcare

Abstract

Big data is revolutionizing biomedical research. Data analysis and interpretation face significant hurdles as a result of remarkable breakthroughs in automated collecting of large-scale molecular and clinical data, necessitating the development of novel computational methodologies. The development of sophisticated systems for the successful use of biomedical big data in personalized medicine (also known as Precision Medicine) would necessitate considerable scientific and technological advances, including infrastructure, engineering, project, and financial management. We examine how the emergence of data-driven approaches has the potential to solve many of these issues, driving the formulation of hypotheses about system behavior and the production of mechanistic models, as well as helping the design of clinical procedures in Personalized Medicine. Big data techniques and technology in healthcare have the potential to add considerable value by improving patient outcomes while cutting costs. Diagnostic imaging, genetic test results, and biometric data are being created and maintained in electronic health records, posing issues in data that is high in volume, diversity, and velocity by nature, demanding innovative storage, management, and processing methods. This necessitates the development of novel, scalable, and extensible big data infrastructure and analytical methodologies that will allow healthcare practitioners to access knowledge specific to each patient, resulting in improved decisions and results. The author has briefly explained the nature of big data, as well as the function of semantic web and data analysis in creating smart data, which provides actionable information to help with tailored health decisions. The most difficult problem, in our opinion, is to develop a system that makes big data robust and smart for healthcare practitioners and patients, resulting in more effective clinical decision-making, improved health outcomes, and, ultimately, cost management. There is a discussion on some of the difficulties in employing large data and argues that a semantic data-driven environment is required to overcome them. We outline a roadmap for empowering customized medicine utilizing big data and semantic web technologies, and we demonstrate our vision with real-use examples. © 2022 Nova Science Publishers, Inc.