Developing Predictive Tools for Precision Medicine: AI Models for Tailored Treatment Plans Using EHR Data

Abstract

Over the past few years, artificial intelligence (AI) and machine learning (ML) algorithms,combined with electronic health record (EHR) data, have become increasingly popular in the sphere of precision medicine and are likely to transform the idea of personalized treatment plans. The study will also investigate how the tools available in the predictive use of the AI models will evolve to develop personalized treatment plans based on the data that gets reflected in the EHR. The project is aimed at resolving the problem of developing accurate, scalable, and interpretable model to predict outcomes of patients and optimize medical decisions. The vast size of the data available on patients is processed using AI algorithms including decision trees, deep learning models, and random forests. The data include medical histories, genetics, and the social-demographic data of the patients. In this paper, the author discusses the different methods that have been used in creating predictive tools and evaluating their performance compared to the traditional models. The results show that AI models allow a high degree of accuracy in predicting treatments far more than the traditional ones. In addition, the research also talks about the consequences of the predictive tools in enhancing patient care as well as operational efficiency and healthcare outcomes. Lastly, the paper also looks at how additional research and substantial challenges involving implication of the AI models in clinical practice exists, namely, when it comes to interpretability of models, ethics-related challenges, and data privacy matters.