Natural Language Processing news, select publications, and downloads

PheMAP–high-throughput Phenotyping by Measured, Automated Profile

Neil Zheng, Wei-Qi Wei
November 4, 2019
Posted in

PheMAP is a general, automatic, and portable approach to enable accurate high-throughput phenotyping within electronic health records (EHR). PheMAP quantifies relationships between phenotypes and relevant clinical concepts represented by standard medical terminologies. For each individual, PheMAP assigns a score and probability of having a particular phenotype from identified related concepts within EHRs.

DEB2 -- Drug Evidence Base, Version 2.0

Joshua Carl Smith
August 23, 2019
Posted in

DEB2 is a medication indication and adverse effect knowledgebase derived from five publicly available sources: the VA’s National Drug File-Reference Terminology, MEDLINE, the US Food and Drug Administration’s drug product labels (via the SIDER2 database), the MedlinePlus consumer health information website, and DrugBank, a manually-curated drug target database. All medications, indications, and adverse effects in DEB2 are represented using the RxNorm and SNOMED-CT terminologies.

KMCI - KnowledgeMap Concept Indexer

August 7, 2015

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.

Tables for Allergy NLP Matching

August 6, 2015

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.

MedEx - A tool for finding medication information

February 2, 2013

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).

MEDI--an Ensemble MEDication Indication Resource

January 1, 2013

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. 

SecTag -- Tagging Clinical Note Section Headers

March 21, 2010

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.