Natural Language Processing news, select publications, and downloads

PheMAP–high-throughput Phenotyping by Measured, Automated Profile

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 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

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.

SecTag -- Tagging Clinical Note Section Headers

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.