Published Paper Details:

t--The paper presents a healthcare assistance system using NLP and Name entity recognition, enabling human-system interaction to resolve basic health related queries before consult- ing a doctor. The main objective is to analyze users symptoms and provide medical suggestions to reduce the time and cost involved in healthcare consultation. This system communicates with users using NLP, allowing for interaction through natural language inputs. It processes the input, extracts relevant keywords, and provides appropriate responses to users queries. Incorporating machine learning techniques, the system leverages an ADR database to offer solutions regarding users' healthcare concerns. Additionally, users can create profiles to specify their symptoms, receive doctor suggestions, and set dosage reminders. The system can assist users in recognizing potential diseases based on pro- vided symptoms. The system uses Neural networks, specifically deep learning models such as CNN or RNNs, are integrated into the text classification and decision-making process. These networks are trained on vast medical datasets, enabling the system to understand complex medical language and detect patterns in user input. Through these neural networks, the system can accurately interpret symptoms, match them to potential diseases, and suggest appropriate next steps.