Health information standards and discovery research focuses on the development of methods to gain insights from large health databases while learning the strengths and weaknesses of datasets and improving them, when possible. This area of research assesses whether specific standards are fit for purpose (e.g., quality assurance and interoperability assessments of biomedical terminologies) and investigates standards in action (e.g., in support of tasks such as natural language processing, annotation, data integration, and mapping across terminologies).
Lister Hill National Center for Biomedical Communication's (LHNCBC) natural language processing (NLP), or text mining, research focuses on the development and evaluation of computer algorithms for automated text analysis. This area of research works primarily with text from the biomedical literature or electronic medical records and examines a wide variety of NLP tasks, including information extraction, literature searches, question answering, and text summarization.
Image processing focuses on data science research in biomedical image and signal processing, artificial intelligence, and machine learning to support automated clinical decision-making in disease screening and diagnostics. This area of research includes image and text analysis for clinical research, exploration of visual content relevant to disease in images and video, and visual information retrieval for embedding automated decision-support systems in diagnostic and treatment pathways.