Trietler QZ

There are a number of Natural Language Processing (NLP) annotation and Information Extraction (IE) systems and platforms that have been successfully used within the medical domain. Although these groups share components of their systems, there has not been a successful effort in the medical domain to codify and standardize either the syntax or semantics between systems to allow for interoperability between annotation tools, NLP tools, IE tools, corpus evaluation tools and encoded clinical documents. There are two components to a successful interoperability standard: an information and a semantic model.

Objective

The Consortium for Healthcare Informatics Research, a Department of Veterans Affairs (VA) Office of Research and Development is sponsoring the development of a standard ontology and information model for Natural Language Processing interoperability within the biomedical domain.

Pro-WATCH (protecting war fighters using algorithms for text processing to capture health events), a syndromic surveillance project for veterans of operation enduring freedom (OEF)/operation Iraqi freedom (OIF), includes a task to identify medically unexplained symptoms (MUS). The v3NLP entity extraction tool is being customized to identify symptoms within VA clinical documents, and then refined to assign duration. The identification of medically unexplained symptoms and the aggregation of this information across documents by patient’s is not addressed here.

Objective

Pro-WATCH (protecting war fighters using algorithms for text processing to capture health events), a syndromic surveillance project, includes a task to identify medically unexplained symptoms. The v3NLP entity extraction tool is being customized to identify symptoms, then to assign duration assertions to address part of this project. The v3NLP tool was recently enhanced to find problems, treatments, and tests for the i2b2/VA challenge. The problem capability is being further refined to find symptoms. Machine learning models will be developed using an annotated corpus currently in development to find duration assertions.