Big Data-Driven Personalized Medicine: Integrating Genomics and Clinical Records Using ML

Sai Teja Nuka

Abstract: The advent of next - generation sequencing technology has led to an exponential growth of genomic data. At the same time, the development of high - throughput methods for patient characterization, recording environmental factors around patients, and collating clinical data has led to an immense increase in clinical records. Analysis of large amounts of such diverse data can provide deeper biological insights about diseases and identify personalized treatments. Integrating and analyzing genomic data and clinical records from heterogeneous sources in a scalable and rapid manner pose significant technical challenges. In contrast to batch learning methods that require data storage and centralized processing, which are often inapplicable to medical data, online learning can process streaming data. Integrating them through online - learning - based systems can help analyze diverse data in a rapid and responsive manner without storage requirements. This is particularly reliable in a big data context, where online learning methods can scale even beyond data diversity to large data volume and unmatched data levels. It is therefore better suited for analyzing genomic information along with clinical and imaging data and for integrating information from diverse and concurrent data sources. In a data war, multiple health organizations develop ML - driven solutions to gain competitive advantages. A robust analysis and matching ML framework that guarantees data privacy allows organizations to compete with each other without the risk of data leaks. This guarantees data usage fairness and rewards the protection of patients? privacy. It also maintains the integrity of the institutional review board (IRB) process, which states that patient data will not leave the premises of institutions. Furthermore, it significantly reduces the risk of mass data breaches, which can have devastating repercussions on both patients and health organizations. Such reputations may include the loss of trust, reduced patient willingness to share data, and legal prosecution. In a similar vein, personalized medicine requires more diversity in treatment and diagnosis as opposed to traditional one - drug - fits - all strategies. Where there are millions of candidate models and parameters that can ingeniously predict treatment effects on prognosis but inadvertently introduce more complexities and uncertainties, a powerful framework that efficiently identifies reasonable explanations remains elusive.

Keywords: Personalized medicine, Genomic data integration, Clinical data mining, Big data healthcare, Machine learning in genomics, Predictive analytics in medicine, Multi - omics data analysis, Precision medicine algorithms, Genomic clinical data fusion, AI in personalized treatment, Data - driven patient stratification, Health informatics ML, Biomarker discovery ML, Electronic health record (EHR) analytics, ML models for disease prediction