LEARNING A DIAGNOSTIC STRATEGY ON MEDICAL DATA WITH DEEP REINFORCEMENT - Dodda Mohan Seshu; Bobba Veeramallu (Utilitas Mathematica, 2025)
The healthcare industry operates with ongoing demands to provide precise diagnostic services at lower costs despite increasing expenses. The development of medical technology combined with diagnostics testing has created an exponential increase of available tests and procedures. Better diagnostic accuracy is a result of this advancement but the increase in healthcare costs has occurred as well. Healthcare systems together with medical practitioners experience an essential challenge which involves selecting diagnostic tests correctly throughout the appropriate timing to minimize duplicate testing. Due to limited resources and increased healthcare service demand this becomes an essential matter. The project "Healthcare Diagnosis Optimization with Reinforcement Learning" employs RL methodologies to sustain cost-effective diagnosis test selections as a solution for healthcare intricacies. This information system performs diagnostic analysis on past patient records to learn from previous medical evaluations thus minimizing redundant testing costs while maintaining high quality medical care standards. The fundamental principle behind this project demonstrates that Reinforcement Learning can train an agent to choose actions in unpredictable environments whose outcomes consist of patient diagnoses. An RL agent functions within a system that handles medical patient data and learns from continuing interactions which cost-effective tests provide superior patient results and healthcare cost savings. A feedback mechanism in this model helps the system assess diagnostic test effectiveness of agent-selected choices to optimize decision making. The goal of this project is twofold: first, to improve the accuracy and efficiency of medical diagnoses, and second, to reduce unnecessary healthcare spending. By training the RL agent to prioritize diagnostic tests that provide the most relevant information at the lowest cost, the system can recommend tests that offer the best diagnostic value for the patient. At the same time, it discourages the selection of expensive or redundant tests that contribute to rising healthcare costs without offering significant additional benefit. One of the key benefits of using Reinforcement Learning for this task is its ability to handle large and complex datasets. With access to historical patient data, the RL agent is capable of learning patterns and correlations that might not be immediately obvious to human doctors or healthcare providers. The model can adapt over time as it receives more data, fine-tuning its decision-making abilities to optimize for both clinical accuracy and cost-efficiency.