Introduction
There are many complex challenges for data interoperability in healthcare. One of them is the ability to search complex radiology images and data to identify patients matching specified characteristics. There are millions of radiology images with doctor's impressions available. Researchers and scientists must categorize and filter out images relevant to their work. This task is not trivial and can be facilitated by using natural language processing (NLP) and AI, in addition to scalable, efficient search engines and other data processing technologies.
Methods
Our team partnered with one of the world’s largest image exchange and medical evidence network providers to develop a sophisticated application for indexing and searching image notes and DICOM tags. The core NLP components contain Melax Tech’s proprietary and customizable artificial intelligence (AI) and machine learning (ML) models for de-identification and concept mapping functionality. In addition, we used state-of-the-art algorithms to process unstructured data and index radiology images based on categories and keywords.
VITAL, one of our core products, served as a starting point for developing a web application with advanced data search and visualization capabilities. This application, integrated with our NLP engine, allows users to quickly search the enormous and rapidly growing inventory of imaging data to improve the effectiveness of real-world evidence (RWE) programs.
Conclusion
Melax Tech’s expertise and technology in NLP solutions can support the search and indexing of large amounts of unstructured medical text and can help you harness that information to gain insights that will benefit your organization. We employ sophisticated methods from the field of artificial intelligence (AI) that includes deep-learning methods, and we work with numerous advanced healthcare data models and clinical terminology systems.
To learn more about indexing and searching image notes and DICOM tags using Melax Tech's NLP solutions, request a demo today!
