Deep Learning-Enhanced Medical System for Disease Prediction, Medication Recommendation, and Intelligent Question-Answering Based on Patient Symptomatology

Deep Learning (DL) is branch of machine learning that emulates the architecture and functioning of the human brain through neural networks to process and analyzes large amounts of data and extract meaningful patterns and insight. This study presents an innovative medical system that leverages deep learning and natural language processing (NLP) techniques to predict diseases based on symptomatology and provide personalized health suggestions, which involves an indepth design of a deep learning-driven medical assistant platform, which will feature the integration of state-of-the-art deep learning models with multiple NLP techniques and models for predicting the disease that a patient may have just by describing the symptoms, prescribing medication, and answering medical-related questions. This project provides a long lasting solutions to the various healthcare issues, like; assisting medical professionals in decision-making quickly, likewise assists patient with answers to medical queries, also assists patient by providing instantaneous, accurate, and personalized health suggestions with pending approval from the doctors and professionals. Bidirectional Encoder Representations from Transformers (BERT) like models will be adopted to answer questions and classify diseases together Long Short-Term Memory (LSTM) for classification task, with BERT also for the remaining tasks. Health access is further facilitated while reducing chances of misdiagnosis that lead to bad outcomes for patients because of intelligent automation. The use of machine learning models allows adaptation to new data, and therefore the system is robust and scalable. The study utilizes TF-IDF vectorization with Hugging Face Transformers, PyTorch, Scikit-learn, Pandas, NLTK libraries, hugging face trainer class and also accuracy, precision, recall f1-score and exact match metrics. The models attained high scores in various metrics which make all the tasks efficient to use by both medical and non-medical personnel in some context.