LHNCBC's Natural Language Processing research and development improves search and retrieval and facilitates discovery through advances in analyzing biomedical texts, graphical presentation of results, and multi-language search.
BabelMeSH and PICO (Patient, Intervention, Comparison, and Outcome) Linguist are multi-language tools for searching MEDLINE/PubMed. 13 languages, including character-based languages, are supported. Recent enhancements include a query using more than one language and retrieving citations in more than one language.
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.
Computational de-identification uses natural language processing (NLP) tools and techniques to recognize patient-related individually identifiable information (e.g. names, addresses, and telephone and social security numbers) in the text, and redacts them. In this way, patient privacy is protected and clinical knowledge is preserved.
The Indexing Initiative (II) project investigates language-based and machine learning methods for the automatic selection of subject headings for use in both semi-automated and fully automated indexing environments at NLM. Its major goal is to facilitate the retrieval of biomedical information from textual databases such as MEDLINE.
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.
LHNCBC's Lexical Systems Group develops and maintains the SPECIALIST lexicon and the tools that support and exploit it. The SPECIALIST Lexicon and NLP Tools are at the center of NLM's natural language research, providing a foundation for all our natural language processing efforts.
PubMed for Handhelds research brings medical information to the point of care via devices like smartphones. This includes developing algorithms and public-domain tools for searching by text message (askMEDLINE and txt2MEDLINE), applying clinical filters (PICO) and viewing summary abstracts (The Bottom Line and Consensus Abstracts) in MEDLINE/PubMed, and evaluating the use of these tools in Clinical Decision Support.