Duvall Scott

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.

Medically unexplained syndromes (MUS) are conditions that are diagnosed on the basis of symptom constellations and are characterized by a lack of well-defined pathogenic pathways. The three most common MUS are chronic fatigue syndrome, irritable bowel syndrome, and fibromyalgia. Different types of persistent symptoms, originating from different organ systems, characterize these syndromes. Patients often meet the criteria for more than one MUS.

Objectives

We sought to develop a guideline and annotation schema that can be consistently applied to identify MUS found in VA clinical documents. These efforts will support building a reference standard used for training and evaluation of a Natural Language Processing system developed for automated symptom extraction. Our overarching goal is to characterize the occurrence of MUS in Operation Enduring Freedom/Operation Iraqi Freedom veterans.

Homelessness in general is a major issue in the US today. The risk factors of homelessness are myriad, including inadequate income, lack of affordable housing, mental health and substance abuse issues, lack of social support, and nonadherence to treatment/follow-up appointments. Early identification of these factors from clinical documents may help detect or even predict homelessness cases, allowing adequate intervention and prevention measures.

Objective

 We demonstrate a semi-automated approach to induce and curate lexical domain knowledge for identification of evidence and risk factors for homelessness found in VA clinical documents. This domain knowledge can be used to support training and evaluation of automated methods such as Natural Language Processing (NLP) systems for detection and prediction of homelessness among veterans. This could serve as a proxy for public health and other surveillance involving homeless individuals. Similar methods could be used to identify other conditions of interest.