Samore M

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.

Epidemiological information realized by modern disease surveillance systems offers great potential for supporting clinical decision-making. Providing health practitioners with population-based, pathogen-specific information about regional communicable infectious disease epidemiology can engender enhanced knowledge about specific pathogens, which may, in turn, lead to improved clinical performance. To enhance the pathogen-specificity of Utah’s surveillance system, which includes tracking syndromes and notifiable diseases, we developed a system that tracks microbiologic testing in Utah’s largest health care delivery system.

Objective

The objective of this study is to describe a system 'Germ Watch' that provides information about the regional activity of common communicable infectious diseases.

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.