Bray Bruce

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.

The research reported in this paper is part of a larger effort to achieve better signal-to-noise ratio, hence accuracy, in pharmacovigilance applications. The relatively low frequency of occurrence of adverse drug reactions leads to weak causal relations between the reaction and any measured signal. We hypothesize that by grouping related signals, we can enhance detection rate and suppress false alarm rate.

Objective

ICD-9 codes are commonly used to identify disease cohorts and are often found to be less than adequate. Data available in structured databasesFlab test results, medications etc.Fcan supplement the diagnosis codes. In this study, we describe an automated method that uses these related data items, and no additional manual annotations to more accurately identify patient cohorts.