NLP

Natural Language Processing
An accurate computable representation of food and drug allergy is essential for safe healthcare. We developed and evaluate a SQL-based method to map free-text allergy/adverse reaction entries to structured entries, using RxNorm as the target vocabulary.  The system was developed and tested using a perioperative management system using a training set of 24,599 entries and a test set of 24,857 entries from Vanderbilt University.
MEDI (MEDication Indication) is an ensemble medication indication resource for primary and secondary uses of electronic medical record (EMR) data.  MEDI was created based on multiple commonly used medication resources (RxNorm, MedlinePlus, SIDER 2, and Wikipedia ) and by leveraging both ontology and natural language processing (NLP) techniques. 

MedEx process free-text clinical records to recognize medication names and signature information, such as drug dose, frequency, route, and duration.  It uses a context-free grammar and regular expression parsing to process free text clinical notes.  After finding medication information, it maps to RxNorm and UMLS concepts at the most specific match it can find (e.g., medication name + strength would be preferred to medication name alone). It has been applied in 2009 i2b2 Medication Extraction challenge, placing second, and formally evaluated on Vanderbilt discharge summaries and clinical notes.

Executable versions available for Linux and Windows below.

The KnowledgeMap Concept Indexer (KMCI) is the underlying natural language processing engine used in the KnowledgeMap and Learning Portfolio website, and has been used for many clinical and genomic research studies.  It identifies biomedical concepts, mapped to Unified Medical Language System concepts, from natural language documents and clinical notes.

Clinical notes are often divided into sections, or segments, such as "history of present illness" or "past medical history." These sections often have subsections as well, such as the "cardiovascular exam" section of the "physical exam." One can gain greater understanding of clinical notes by recognition of the section in which a concept lives. For instance, both a "past medical history" and the "family medical history" sections can contain a list of diseases, but the context decribes very different import to the patient about whom the note was written. Section tagging is an important early step in natural language processing applications applied to clinical notes.

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