ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2007
Evaluation of a chief complaint pre-processor for biosurveillance.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Using natural language processing in emergency medicine health service research: A systematic review and meta-analysis.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2024Pooled it
- Generalized Extraction and Classification of Span-Level Clinical Phrases.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Chief complaint-based performance measures: a new focus for acute care quality measurement.Annals of emergency medicine · 2015Article
- Using chief complaints for syndromic surveillance: a review of chief complaint based classifiers in North America.Journal of biomedical informatics · 2013Review
- Optimizing A syndromic surveillance text classifier for influenza-like illness: Does document source matter?AMIA ... Annual Symposium proceedings. AMIA Symposium · 2008Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Emergency Department (ED) chief complaint (CC) data are key components of syndromic surveillance systems. However, it is difficult to use CC data because they are not standardized and contain varying semantic and lexical forms for the same concept. The purpose of this project was to revise a previously-developed text processor for pre-processing CC data specifically for syndromic surveillance and then evaluate it for acute respiratory illness surveillance to support decisions by public health epidemiologists. We evaluated the text processor accuracy and used the results to customize it for respiratory surveillance. We sampled 3,699 ED records from a population-based public health surveillance system. We found equal sensitivity, specificity, and positive and negative predictive value of syndrome queries of data processed through the text processor compared to a standard keyword method on raw, unprocessed data.
Indexed as
Identifiers
18693934PMC2655903What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.