Wendy W. Chapman

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Narrative reports in medical records contain a wealth of information that may augment structured data for managing patient information and predicting trends in diseases. Pertinent negatives are evident in text but are not usually indexed in structured databases. The objective of the study reported here was to test a simple algorithm for determining whether(More)
In this paper we describe an algorithm called ConText for determining whether clinical conditions mentioned in clinical reports are negated, hypothetical, historical, or experienced by someone other than the patient. The algorithm infers the status of a condition with regard to these properties from simple lexical clues occurring in the context of the(More)
OBJECTIVE Automatically identifying findings or diseases described in clinical textual reports requires determining whether clinical observations are present or absent. We evaluate the use of negation phrases and the frequency of negation in free-text clinical reports. METHODS A simple negation algorithm was applied to ten types of clinical reports(More)
Discharge summaries and other free-text reports in healthcare transfer information between working shifts and geographic locations. Patients are likely to have di culties in understanding their content, because of their medical jargon, non-standard abbreviations, and ward-speci c idioms. This paper reports on an evaluation lab with an aim to support the(More)
Applications using automatically indexed clinical conditions must account for contextual features such as whether a condition is negated, historical or hypothetical, or experienced by someone other than the patient. We developed and evaluated an algorithm called ConText, an extension of the NegEx negation algorithm, which relies on trigger terms,(More)
This paper describes the SemEval-2014, Task 7 on the Analysis of Clinical Text and presents the evaluation results. It focused on two subtasks: (i) identification (Task A) and (ii) normalization (Task B) of diseases and disorders in clinical reports as annotated in the Shared Annotated Resources (ShARe)1 corpus. This task was a follow-up to the ShARe/CLEF(More)
This paper reports on the 2nd ShARe/CLEFeHealth evaluation lab which continues our evaluation resource building activities for the medical domain. In this lab we focus on patients’ information needs as opposed to the more common campaign focus of the specialised information needs of physicians and other healthcare workers. The usage scenario of the lab is(More)
OBJECTIVE Develop and evaluate a natural language processing application for classifying chief complaints into syndromic categories for syndromic surveillance. INTRODUCTION Much of the input data for artificial intelligence applications in the medical field are free-text patient medical records, including dictated medical reports and triage chief(More)
This issue of JAMIA focuses on natural language processing (NLP) techniques for clinical-text information extraction. Several articles are offshoots of the yearly ‘Informatics for Integrating Biology and the Bedside’ (i2b2) (http://www.i2b2.org) NLP shared-task challenge, introduced by Uzuner et al (see page 552) and cosponsored by the Veteran’s(More)
INTRODUCTION Computer-based outbreak and disease surveillance requires high-quality software that is well-supported and affordable. Developing software in an open-source framework, which entails free distribution and use of software and continuous, community-based software development, can produce software with such characteristics, and can do so rapidly.(More)