The Communications Engineering Branch (CEB) is part of the Lister Hill National Center for Biomedical Communications, an intramural R&D division of the U.S. National Library of Medicine. Our mission is to conduct research and development directed toward mission-critical tasks at NLM and NIH, such as cancer research, document delivery, digital preservation, and automated ways of building resources such as MEDLINE.® All software products developed by our researchers are freely available.
Research in machine learning and artificial intelligence (ML/AI) algorithms aims to improve computer-aided disease detection, accuracy and reliability. We develop novel computational solutions to analyze chest x-rays (CXR) and screen for cardiopulmonary diseases with a special interest in pulmonary TB in HIV+ population.
Multiple projects in this area continue to promote the development, enhancement, and adoption of clinical vocabulary standards. Inter-terminology mapping promotes the use of standard terminologies by creating maps to administrative terminologies, which allows re-use of encoded clinical data.
The consumer health question answering project was launched to support NLM customer services that receive about 90,000 requests a year from a world-wide pool of customers.
Large database collections of clinical data -- from longitudinal research projects, electronic medical records, and health information exchanges -- provide opportunities to examine controversial findings from smaller scale clinical studies and to conduct retrospective epidemiological studies in areas that lack clinical trials.
The goal of our work in Biomedical Imaging is two-fold: One, to develop advanced imaging tools for biomedical research in partnership with the National Cancer Institute and other organizations. Secondly, to conduct research in Content Based Image Retrieval (CBIR) to index and retrieve medical images by image features (e.g., shape, color and texture), augmented by textual features as well.
This system automatically augments a patient's Electronic Health Record (EHR) with pertinent information from NLM resources. The software runs as background agents, both at a hospital and at NLM. The hospital uses our APIs to integrate the search setup and to display and store results in their existing EHR system.
To improve malaria diagnostics, we are developing a fully-automated system for parasite detection and counting in blood films in collaboration with NIH’s National Institute of Allergy and Infectious Diseases (NIAID) and Mahidol-Oxford University.
This project seeks to improve information retrieval from collections of full-text biomedical articles, images, and patient cases, by moving beyond conventional text-based searching to combining both text and visual features.
RIDeM provides access to key facts needed to support clinical decision making. The facts are extracted from biomedical literature and clinical text sources. The development of the Repository is guided by the Evidence Based Medicine (EBM) principles for finding and appraising information.