General Symptom Extraction from VA Electronic Medical Notes.

There is need for cataloging signs and symptoms, but not all are documented in structured data. The text from clinical records are an additional source of signs and symptoms. We describe a Natural Language Processing (NLP) technique to identify symptoms from text.

An Innovative Method for Monitoring Food Quality and the Healthfulness of Consumers' Grocery Purchases.

This study presents a method laying the groundwork for systematically monitoring food quality and the healthfulness of consumers' point-of-sale grocery purchases. The method automates the process of identifying United States Department of Agriculture (USDA) Food Patterns Equivalent Database (FPED) components of grocery food items. The input to the process is the compact abbreviated descriptions of food items that are similar to those appearing on the point-of-sale sales receipts of most food retailers.

Exploiting the UMLS Metathesaurus for extracting and categorizing concepts representing signs and symptoms to anatomically related organ systems.

Objective: To develop a method to exploit the UMLS Metathesaurus for extracting and categorizing concepts found in clinical text representing signs and symptoms to anatomically related organ systems. The overarching goal is to classify patient reported symptoms to organ systems for population health and epidemiological analyses.

Materials And Methods: Using the concepts' semantic types and the inter-concept relationships as guidance, a selective portion of the concepts within the UMLS Metathesaurus was traversed starting from the concepts representing the highest level organ systems.

Towards Measuring the Food Quality of Grocery Purchases: an Estimation Model of the Healthy Eating Index-2010 Using only Food Item Counts.

Measuring the quality of food consumed by individuals or groups in the U.S. is essential to informed public health surveillance efforts and sound nutrition policymaking.

Scaling Out and Evaluation of OBSecAn, an Automated Section Annotator for Semi-Structured Clinical Documents, on a Large VA Clinical Corpus.

"Identifying and labeling" (annotating) sections improves the effectiveness of extracting information stored in the free text of clinical documents. OBSecAn, an automated ontology-based section annotator, was developed to identify and label sections of semi-structured clinical documents from the Department of Veterans Affairs (VA). In the first step, the algorithm reads and parses the document to obtain and store information regarding sections into a structure that supports the hierarchy of sections.

Computational Science and Engineering Online (CSE-Online): a cyber-infrastructure for scientific computing.

With the expansion of the Internet and World Wide Web (or the Web), research environments have changed dramatically. As a result, the need to be able to efficiently and securely access information and resources from remote computer systems is becoming even more critical. This paper describes the development of an extendable integrated Web-accessible simulation environment for computational science and engineering called Computational Science and Engineering Online (CSE-Online; http://cse-online.