Auto-Complete System for Doctors' History-Making Using NLG with LSTM Model

->Developed an auto-complete feature tailored for doctors' history-making, utilizing Natural Language Generation (NLG). ->Implemented the Long Short-Term Memory (LSTM) model to predict and generate text sequences, enhancing the efficiency of medical documentation. ->Integrated the system with a Gradio-based user interface for real-time interaction and text prediction. ->Utilized a dataset of medical prescriptions to train the model, ensuring contextually accurate and relevant suggestions. ->Conducted extensive testing and optimization to ensure high accuracy and performance in the auto-complete functionality.