Big Data Integration with Machine Learning Towards Public Health Records and Precision Medicine

Abstract

Contemporary biological science studies associate the term 'precision' with health care and public health (PH) with concepts such as big data, data integration, and Machine Learning (ML). Technological breakthroughs enable the aggregation and integration of extensive heterogeneous information from many sources, including genomic sequences, social media postings, Public Health Records (PHR), and wearable devices. Moreover, sophisticated algorithms facilitated by high-performance computers enable the converting of big data into intelligence. Notwithstanding this progress, several obstacles persist in realizing Precision Medicine (PM) and PHR for the betterment of individuals and populations. A primary objective of PM is to incorporate and integrate big data from various PHRs regarding the cellular and ecological roots of disease into analytical backgrounds, facilitating the creation of personalized, context-specific diagnosis and treatment plans. In this context, artificial intelligence (AI) and ML methodologies may be used to develop analytical representations of intricate diseases to predict individualized health situations and outcomes. These frameworks must include the extensive variability among people regarding their genetic predispositions and contextual and social variables. Computational methodologies in medicine must effectively handle, display, and incorporate big data, including organized and unorganized forms. Effective big data integration and organization are essential for the effective use of ML methodologies in PM. Numerous obstacles emerge in developing effective medical big data analytics systems, given the present stringent performance requirements in PM, alongside limitations in time, computing resources, and bioethical considerations.