Precision Medicine With Data-driven Approaches: A Framework For Clinical Translation

Abstract

Precision medicine-based approaches differentiate themselves by taking into consideration subpopulation variability (e.g. genetic variations, age, gender, race, addictions). To date, traditional models, such as Trial-and-Error Dosing, Empirical Treatment Guidelines, Statistical and Actuarial Models, Pathophysiological Models, and Clinical Judgment and Experience, have been generalized in healthcare fields. However, more comprehensive and innovative technologies are required besides these conventional modelings, which are subject to various limitations such as low efficiency and incapability of processing complex biological systems. Here we review diverse machine learning (ML) algorithms integrated with big data and omics and its applications in various aspects of precision medicine. ML is the branch of artificial intelligence (AI), which has been rapidly developed and highlighted as a promising method to decrease diagnostic errors and aid clinicians with decision-making in recent decades. We focused on applications of ML models such as support vector machine (SVM), K Nearest Neighbor (KNN) random forest (RF), convolutional neural networks (CNNs) and deep learning in drug toxicity prediction, cardiovascular diseases, neurodegenerative diseases, and cancer therapies within precision medicine and specific benefits and challenges of each. This review provides insights on the wider utilization in clinical environments by recognizing current advantages that are expected to expand the scope of AI-driven methods and issues that need to be addressed for further studies.