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Patients report their symptoms and subjective experiences in their own words. These expressions may be clinically meaningful yet are difficult to capture using automated methods. We annotated subjective symptom expressions in 750 clinical notes from the Veterans Affairs EHR. Within each document, subjective symptom expressions were compared to mentions of(More)
reproduction in any medium, provided the original work is properly cited. Objective To highlight the importance of templates in extracting surveillance data from the free text of electronic medical records using natural language processing (NLP) techniques. Introduction The main stay of recording patient data is the free text of electronic medical records(More)
Objective This paper describes a new congestive heart failure (CHF) treatment performance measure information extraction system - CHIEF - developed as part of the Automated Data Acquisition for Heart Failure project, a Veterans Health Administration project aiming at improving the detection of patients not receiving recommended care for CHF. Design CHIEF(More)
"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(More)
Templated boilerplate structures pose challenges to natural language processing (NLP) tools used for information extraction (IE). Routine error analyses while performing an IE task using Veterans Affairs (VA) medical records identified templates as an important cause of false positives. The baseline NLP pipeline (V3NLP) was adapted to recognize negation,(More)
Early warning indicators to identify US Veterans at risk of homelessness are currently only inferred from administrative data. References to indicators of risk or instances of homelessness in the free text of medical notes written by Department of Veterans Affairs (VA) providers may precede formal identification of Veterans as being homeless. This(More)
Information retrieval algorithms based on natural language processing (NLP) of the free text of medical records have been used to find documents of interest from databases. Homelessness is a high priority non-medical diagnosis that is noted in electronic medical records of Veterans in Veterans Affairs (VA) facilities. Using a human-reviewed reference(More)
Efforts to improve the treatment of congestive heart failure, a common and serious medical condition, include the use of quality measures to assess guideline-concordant care. The goal of this study is to identify left ventricular ejection fraction (LVEF) information from various types of clinical notes, and to then use this information for heart failure(More)