MACHINE LEARNING BASED MEDICAL INFORMATION RETRIEVAL SYSTEMS

Abstract

As many fields progress with the assistance of cognitive computing, the field of health care is also adapting, providing many benefits to all users. However, advancements in this area are hindered by several challenges such as the void between user queries and the knowledge base, query mismatches, and range of domain knowledge in users. In this research, we explore existing methodologies as well as look into existing real-life applications that are used in the medical field today. We also look into specific challenges and techniques that can be used to overcome these barriers, specifically related to cognitive computing in the medical domain. Future information retrieval (IR) models that can be tailored specifically for medically intensive applications which can handle large amounts of data are explored as well. The purpose of this work is to give the reader an in-depth understanding of artificial intelligence being used in the medical field today, as well as future possibilities in the domain. The models and techniques designed and discussed in this research can help provide a framework, or starting point for those interested in effectively developing, maintaining, and using these models to help improve the quality of health-care. Furthermore, we explore the development process of such a model and discuss the steps including data collection, processing, model creation, and also improvement.