Real-Time Prediction of Patient Recovery Time using AI-driven Optimization Techniques in Healthcare

This study was designed to construct a real-time prediction model for the recovery of patients in hospitals through AI-based optimization algorithms. The studies employed electronic health records for data collection and artificial intelligence algorithms, including decision trees, random forest, and support vector regression, to develop the prediction model. The model was subsequently trained and tested on a patient record dataset to assess the solution's accuracy and value. Study results: The study showed that, on average, 85% of patient recovery time could be predicted in real time via the developed model. This could have considerable connotations for healthcare providers regarding how to treat and prioritize patients best. In addition, the abstract points to potential advantages of using AI-powered optimization of healthcare practices, such as better efficiency in a system, more minor costs, and higher quality patient care.) It also presents further exploration and model enhancement areas, including additional patient information or more advanced AI models.