Machine Learning Classification Approach for Empirical Analysis of Therapeutic Drug Monitoring

Advancement in Artificial intelligence & machine learning have spurred renewed interest in each & every industry. The intelligent automation is a very important reason for the fast development of any Industry. Machine learning provides support to take decisions in certain trained environments. Machine leaning uses various algorithms to analyze, classify and predict defined outcomes. TDM-Therapeutic drug monitoring is an effective treatment to minimize potential side effects during patient medical condition. It keeps measurement of mentioned drugs at time series in patients’ bloodstream. Generally, it focuses on narrow therapeutic ranges. TDM is essential when drugs may affect adverse effects. This study starts with Machine leaning & its classification approaches. It focuses on therapeutic drug monitoring & its analytical & practical issues. It also covers the recent advances in TDM Practice. Finally, it experiments with a Machine learning classification approach on historical clinical records of adverse effects. It also covers the proposed advancement of Machine learning enabled TDM using Logistic Regression & Naive bayes for adverse drug effects in patients.