ArticleJournal of biomedical informatics2019
Chief complaint classification with recurrent neural networks.
Article in Journal of biomedical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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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.
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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.
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Who cites it
14 citing papers in PubMed.
- Fine-Tuning Large Language Models for Structured Extraction of Infectious Disease-Related Information From Clinical Notes in Japanese Primary Care: Development and Internal Validation Study.JMIR formative research · 2026Article
- Asyndromic surveillance of New York City emergency department diagnoses with the tree-temporal scan statistic.Journal of public health research · 2026Article
- Advocating the potential of artificial intelligence for syndrome discovery in syndromic surveillance systems: A scoping review.iScience · 2026Review
- An Intelligent System for Classifying Patient Complaints Using Machine Learning and Natural Language Processing: Development and Validation Study.Journal of medical Internet research · 2025Article
- Article
- Innovations in public health surveillance: An overview of novel use of data and analytic methods.Canada communicable disease report = Releve des maladies transmissibles au Canada · 2024Article
- The Use and Structure of Emergency Nurses' Triage Narrative Data: Scoping Review.JMIR nursing · 2023Article
- Influenza, dengue and common cold detection using LSTM with fully connected neural network and keywords selection.BioData mining · 2022Article
- Prediction of the Vaccine-derived Poliovirus Outbreak Incidence: A Hybrid Machine Learning Approach.Scientific reports · 2020Article
- Natural Language Processing to Extract Meaningful Information from Patient Experience Feedback.Applied clinical informatics · 2020Article
- Article
- Twitter mining using semi-supervised classification for relevance filtering in syndromic surveillance.PloS one · 2019Article
- Natural language generation for electronic health records.NPJ digital medicine · 2018Article
- Performance of emergency triage prediction of an open access natural language processing based chatbot application (ChatGPT): A preliminary, scenario-based cross-sectional study.Turkish journal of emergency medicineArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Syndromic surveillance detects and monitors individual and population health indicators through sources such as emergency department records. Automated classification of these records can improve outbreak detection speed and diagnosis accuracy. Current syndromic systems rely on hand-coded keyword-based methods to parse written fields and may benefit from the use of modern supervised-learning classifier models. In this paper, we implement two recurrent neural network models based on long short-term memory (LSTM) and gated recurrent unit (GRU) cells and compare them to two traditional bag-of-words classifiers: multinomial naïve Bayes (MNB) and a support vector machine (SVM). The MNB classifier is one of only two machine learning algorithms currently being used for syndromic surveillance. All four models are trained to predict diagnostic code groups as defined by Clinical Classification Software, first to predict from discharge diagnosis, and then from chief complaint fields. The classifiers are trained on 3.6 million de-identified emergency department records from a single United States jurisdiction. We compare performance of these models primarily using the F
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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.