Authors: S.Akhila, G. Shirisha
Abstract: The extraction of meaningful and valuable medical knowledge from large-scale healthcare data has become a major research focus for supporting clinical decision-making. Among these advancements, personalized medicine recommendation has emerged as a significant area of study, aiming to identify the most appropriate medications for individual patients based on their specific health conditions. Such recommendation systems assist healthcare professionals in making informed prescribing decisions while minimizing the risk of medication-related complications, thereby attracting considerable attention from the research community. This survey presents a comprehensive review of personalized medicine recommendation by first defining the problem and its fundamental objectives. It then systematically categorizes and analyzes recent recommendation approaches according to four major perspectives: multi-disease medicine recommendation, medicine recommendation using combination patterns, recommendation enhanced with additional medical knowledge, and recommendation driven by patient feedback. Furthermore, the survey discusses commonly adopted evaluation methodologies and performance metrics used to assess these recommendation models. Finally, it highlights the major challenges in personalized medicine recommendation and outlines promising future research directions and emerging development trends in this rapidly evolving field.
