D’Avolio Leonard

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.

Information about disease severity could help with both detection and situational awareness during outbreaks of acute respiratory infections (ARI). In this work, we use data from the EMR to identify patients with pneumonia, a key landmark of ARI severity. We asked if computerized analysis of the free-text of clinical notes or imaging reports could complement structured EMR data to uncover pneumonia cases.

Objective

To improve the surveillance for pneumonia using the free-text of electronic medical records (EMR).