Abstract: The present invention provides solution to the above-mentioned problem in the art by providing a system and a method for an efficient and adaptive disease prediction system as a part of an expert-based online AI symptom-checker. The system may diagnose a wide variety of diseases accurately in an under 3-minute chatbot based conversation. The system architecture comprises of state-based components namely dialogue, patient, utterance, disease and symptom state. The disease prediction module may use a complex knowledge graph coupled with the state information to predict disease scores, predict disease scores based on various factors such as symptom coverage, relative disease importance, diseases priors, risk factors, lab tests, body organs and body systems and further provide mechanisms to arrive at a convergence based on factors such as confidence scores, questions asked and the like.

Abstract: The present invention provides solution to the above-mentioned problem in the art by providing a system and a method for efficiently identifying from a medical domain a plurality of entities that may be designed and engineered for a diagnostic reasoning system or symptom checker. The identified plurality of entities forms nodes of a knowledge graph and may be interconnected to each other with edges representing finely curated weights. The curated weights may form a dynamic repository that can constantly evolves to best reflect the latest medical knowledge. Apart from diseases and symptoms, the system is also capable of extending and scaling to other dimensions of medical knowledge. The knowledge graph thus developed is fundamental to the entire diagnostic system and is used by various components of the diagnostic engine to power the AI-based reasoning.

Abstract: Present disclosure generally relates to computer-assisted medical diagnosis systems, particularly, to system and method for generating adaptive medical queries to determine disease symptoms of a user. System receives inputs from user, in response to queries corresponding to symptoms of disease. Further, system extracts contextual attributes corresponding to medical context, from inputs and identifies batch-wise candidate attributes from extracted contextual attributes, and sorts batch-wise candidate attributes corresponding to each of symptoms. Furthermore, system maps symptoms in sorted batch-wise candidate attributes to disease, by searching medical knowledge database, and calculates disease-symptom weighted score. Additionally, system filters symptoms based on age and gender of user, and sorts symptoms based on calculated disease-symptom weighed score and inter-dependency on other attributes.