Abstract:
With the explosion of healthcare information, there has been a tremendous amount of heterogeneous Textual Medical Knowledge(TMK), which plays an essential role in healthcare information
systems. Existing works for integrating and utilizing the textual medical knowledge mainly focus on straightforward connections establishment and pay less attention to make computers
interpret and retrieve knowledge correctly and quickly.
In our work, we explore a novel model to organize and integrate the TMK into Knowledge
graphs. We then employ a framework to automatically retrieve knowledge in knowledge graphs
with high precision. In our work, we plan to build high quality and comprehensive Medical
Knowledge Graph which is highly scalable. Our Knowledge Graph would be based on the
custom created ontology - MedOnto. Moreover, most of the Medical Knowledge bases do not
focus on the application based aspects of personal health like information about drugs, their
brand names, prices etc. We also develop a WebApp called MedMate which can help doctors
in selecting drugs and common men to get low cost drugs and other informations regarding the
drugs prescribed. As part of the application, we plan to build an efficient recommender system
based on the above topics