Machine Learning Models for Personalized Treatment Pathways in Modern Medicine

Abstract

Individual medicine act as in major modern heath treatment in everywhere, centering on customize healthcare that line up with an the personalized (one to one) genetic history, clinical profile and different life style factors. In opposites, traditional therapeutic approaches-frequently guided by population based standards - ignore variations in disease act and patient specific drug persons. In this case machine learning technique provides powerful tools to interpret difficult and nonlinear patterns hidden within multiple medical datasets, thereby enabling the creation of personalized care plans. In this paper we try to presents a systematically evolution of machine learning techniques. Including deep learning, supervised and unsupervised learning with reinforcement learning, in advancing data driven individual clinical decision making. It demonstrates their applications all over major domains like oncology, cardiology, neurology, pharmacology and psychiatry exploring their potential for improving treatment efficiency. In additional, this paper examines moral, regulatory and technical prompting and outlines emerging research pathway such as explainable AI and combine learning. Overall, this study underscores ML's transformative character in making healthcare more predictive, precise and patient specific.

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