ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2013
Implementation of Emergency Medical Text Classifier for syndromic surveillance.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 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
- Human factors methods in the design of digital decision support systems for population health: a scoping review.BMC public health · 2024Article
- Natural Language Processing of Nursing Notes: An Integrative Review.Computers, informatics, nursing : CIN · 2023Review
- Development and validation of an automated emergency department-based syndromic surveillance system to enhance public health surveillance in Yukon: a lower-resourced and remote setting.BMC public health · 2021Article
- Public Health and Epidemiology Informatics: Recent Research and Trends in the United States.Yearbook of medical informatics · 2015Review
- Accuracy of automatic syndromic classification of coded emergency department diagnoses in identifying mental health-related presentations for public health surveillance.BMC medical informatics and decision making · 2014Article
- Validation of a Syndromic Case Definition for Detecting Emergency Department Visits Potentially Related to Marijuana.Public health reports (Washington, D.C. : 1974)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Public health officials use syndromic surveillance systems to facilitate early detection and response to infectious disease outbreaks. Emergency department clinical notes are becoming more available for surveillance but present the challenge of accurately extracting concepts from these text data. The purpose of this study was to implement a new system, Emergency Medical Text Classifier (EMT-C), into daily production for syndromic surveillance and evaluate system performance and user satisfaction. The system was designed to meet user preferences for a syndromic classifier that maximized positive predictive value and minimized false positives in order to provide a manageable workload. EMT-C performed better than the baseline system on all metrics and users were slightly more satisfied with it. It is vital to obtain user input and test new systems in the production environment.
Indexed as
Identifiers
24551413PMC3900151What 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.