THE INTEGRATION OF COMPUTATIONAL STUDIES IN MEDICAL TRAINING AND NURSING

Personalized treatment plans tailored to individual patient needs have the potential to significantly enhance healthcare outcomes. Machine learning (ML) algorithms can analyze vast amounts of patient data to develop these individualized treatment plans, leading to more precise and effective interventions. This paper explores the methodologies, applications, and challenges of using ML to create personalized treatment plans. We review existing literature, describe various ML techniques, and discuss the potential and limitations of implementing these approaches in clinical settings. The concept of personalized medicine involves tailoring medical treatment to the individual characteristics of each patient. This approach contrasts with the traditional "one-size-fits-all" paradigm, which often fails to account for individual variability in genetics, environment, and lifestyle. The advent of machine learning has opened new avenues for personalized medicine by enabling the analysis of large and complex datasets to uncover patterns that inform individualized treatment strategies. Personalized treatment plans can lead to improved outcomes by ensuring that therapies are specifically suited to the patient's unique profile.