AGENTIC AI FOR PREDICTIVE HEALTHCARE ANALYTICS

Abstract

Agentic Artificial Intelligence is an advanced concept in Artificial Intelligence where systems function as intelligent agents capable of autonomous decision-making, reasoning, and task execution without requiring continuous human intervention. Unlike traditional AI systems that mainly operate reactively by responding only to user inputs, Agentic AI systems are proactive and capable of analyzing situations, making decisions, and performing actions independently to achieve specific goals. This project focuses on developing an Agentic AI-based predictive healthcare analytics system that combines Machine Learning and Natural Language Processing techniques to improve healthcare decision-making and patient analysis. The proposed system is designed to process healthcare-related textual and clinical data, identify patient conditions, predict possible health risks, and assist healthcare professionals in making informed decisions. The system accepts user or patient input in textual form and processes the information using NLP techniques such as tokenization, stop-word removal, stemming, and text normalization. These preprocessing operations improve the quality and consistency of input data before analysis. After preprocessing, the system applies machine learning classification and predictive analytics techniques to identify patient intent, symptoms, and potential healthcare conditions. The Agentic AI framework follows multiple intelligent stages similar to human reasoning processes, including perception, understanding, decision-making, and action execution. Based on the analyzed information, the decision-making module autonomously determines the most appropriate response, recommendation, or predictive healthcare action.