Deepanshu Pandey
Independent Researcher
Madhya Pradesh, India
Abstract
The growing complexity of treating multifactorial diseases such as cancer, cardiovascular conditions, and chronic infections has propelled the need for multidrug therapy (MDT) regimens tailored to individual patient profiles. Artificial Intelligence (AI), particularly expert systems, machine learning algorithms, and probabilistic inference models, has emerged as a powerful enabler for designing such personalized therapies. This manuscript explores AI methodologies developed before mid-2016 that have been used to generate patient-specific drug combinations by analyzing pharmacogenomic data, patient history, drug–drug interactions, and disease progression models. Through a comprehensive literature review, we assess the capability of AI in synthesizing large-scale clinical data to support multidrug regimen optimization, minimize adverse effects, and enhance therapeutic outcomes. Furthermore, the manuscript outlines a methodology integrating Bayesian networks and support vector machines to demonstrate AI’s applicability in generating MDT recommendations. The results suggest promising clinical decision support capabilities, highlighting AI’s transformative role in personalized pharmacotherapy.
Keywords
Artificial intelligence, multidrug therapy, personalized medicine, pharmacogenomics, decision support, expert systems
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