Abstract: An automatic medical knowledge curation system automatically extracts medical knowledge from multiple sources, including medical journals, publications and publication databases, and stores this extracted information in the form of a large-scale medical knowledge graph. The system identifies clinical, health and life insurance risk factor entities and medical management information including disease detection, smoking, alcohol consumption patterns, lifestyle information, diagnosis, prognosis, treatment, measuring, monitoring and reporting. The system determines relationships between clinical entities using machine learning and data mining methods. The system determines relationship strengths and can also determine missing and noisy relationships.

Abstract: A personal medical-bot with a natural language translator implemented on a personal communication device. The medical-bot interacts in natural language with a user respondent/patient who presents a medical problem. The medical-bot includes a natural language translator with an artificial intelligence (AI) module that accepts the natural language inputs, and identifies medically relevant terminologies and their associations. These are fed to the AI for processing generate clinical-based queries to be answered by the patient. The responses are used by the medical-bot to extract medically relevant data for establishing a medical history and enabling a medical diagnosis for the patient. The medical-bot is able to simulate the sequential queries of a doctor or nurse practitioner to arrive at a diagnosis and an immediate treatment plan, which determines the triage to be followed by the patient. A health score for the patient is also determined.