Thompson Sampling Algorithm for Personalized Treatment Recommendations in Healthcare

The Thompson Sampling algorithm's use in individualized healthcare decision-making is examined in this study. Utilizing constantly changing patient data, the algorithm, which is rooted in Bayesian principles, dynamically modifies recommendations for treatment. The study uses a descriptive design, interpretivism, and a deductive method. Secondary data will be utilized for a thorough analysis. The performance metrics, comparative analysis with conventional methods, alongside adaptability of the algorithm are highlighted by the results. The robustness and generalizability are emphasized as we examine sensitivity to data variability. Transparency and ethical considerations are important focal points. Recommendations include developing ethical guidelines, validating in various healthcare settings, dealing with biases, and improving interpretability. The careful integration of Thompson Sampling is guided by these insights, which further the advancement of personalized healthcare.