Lower Respiratory Tract Infections Dataset

The LRTI data is a structured dataset designed for integration into models for building and training conversational chatbots, specifically focused on responding to user queries targeting Lower Respiratory Tract Infections (LRTIs). The data covers a wide range of LRTIs including COVID-19, Tuberculosis, Pneumonia, COPD, Bronchitis, and Asthma. Data Structure The LRTI dataset utilizes an \"intent-based\" structure. Intents represent specific topics or goals users might inquire about. Each intent is categorized by the following elements: Tag: These tags represent the name of the goal or topic, such as \"COVID-19,\" indicating that the intent contains information about COVID-19. Context: Provides details about the specific aspect of the intent being discussed, such as \"Causes of COVID-19.\" Contexts may vary even if the tag remains the same, allowing for a more nuanced understanding of the topic. Patterns: These are the typical questions or phrases a user might ask related to the context, such as \"What causes COVID-19?\" or \"How is COVID-19 spread?\" Patterns have a meaningful relationship with the tag and context and can be analyzed using machine learning algorithms since they perfectly align to each other. Responses: These are the answers provided to users' questions by a trained model (chatbot). They offer explanations related to the patterns of the intent and based on the context.