ArticleJournal of biomedical informatics2015
Comparison of machine learning classifiers for influenza detection from emergency department free-text reports.
Article in Journal of biomedical informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- PrecLLM: A Privacy-Preserving Framework for Efficient Clinical Annotation Extraction from Unstructured EHRs using Small-Scale LLMs.Research square · 2026Article
- RETRACTED: Artificial intelligence for emergency medical care.Health care science · 2025Review
- Artificial Intelligence in Emergency Medicine: A Primer for the Nonexpert.Journal of the American College of Emergency Physicians open · 2025Article
- Online Transfer Learning for RSV Case Detection.Proceedings. IEEE International Conference on Healthcare Informatics · 2024Article
- Supervised Machine Learning Methods for Seasonal Influenza Diagnosis.Diagnostics (Basel, Switzerland) · 2023Article
- Article
- The Aspects of Running Artificial Intelligence in Emergency Care; a Scoping Review.Archives of academic emergency medicine · 2023Article
- Examining the Use of an Artificial Intelligence Model to Diagnose Influenza: Development and Validation Study.Journal of medical Internet research · 2022Article
- Construction of Influenza Early Warning Model Based on Combinatorial Judgment Classifier: A Case Study of Seasonal Influenza in Hong Kong.Current medical science · 2022Article
- Modern Machine-Learning Predictive Models for Diagnosing Infectious Diseases.Computational and mathematical methods in medicine · 2022Review
- Detection and Prevention of Virus Infection.Advances in experimental medicine and biology · 2022Article
- Advances in the Rapid Diagnostic of Viral Respiratory Tract Infections.Frontiers in cellular and infection microbiology · 2022Review
- Disaster and Pandemic Management Using Machine Learning: A Survey.IEEE internet of things journal · 2021Article
- Article
- Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases.NPJ digital medicine · 2021Article
- Machine Learning and Syncope Management in the ED: The Future Is Coming.Medicina (Kaunas, Lithuania) · 2021Review
- Natural language processing and machine learning of electronic health records for prediction of first-time suicide attempts.JAMIA open · 2021Article
- Deployment of artificial intelligence for radiographic diagnosis of COVID-19 pneumonia in the emergency department.Journal of the American College of Emergency Physicians open · 2020Article
- An artificial intelligence approach to COVID-19 infection risk assessment in virtual visits: A case report.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- The Development of the Military Service Identification Tool: Identifying Military Veterans in a Clinical Research Database Using Natural Language Processing and Machine Learning.JMIR medical informatics · 2020Article
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Authors and funding
6 authors.
Funding
Abstract
Influenza is a yearly recurrent disease that has the potential to become a pandemic. An effective biosurveillance system is required for early detection of the disease. In our previous studies, we have shown that electronic Emergency Department (ED) free-text reports can be of value to improve influenza detection in real time. This paper studies seven machine learning (ML) classifiers for influenza detection, compares their diagnostic capabilities against an expert-built influenza Bayesian classifier, and evaluates different ways of handling missing clinical information from the free-text reports. We identified 31,268 ED reports from 4 hospitals between 2008 and 2011 to form two different datasets: training (468 cases, 29,004 controls), and test (176 cases and 1620 controls). We employed Topaz, a natural language processing (NLP) tool, to extract influenza-related findings and to encode them into one of three values: Acute, Non-acute, and Missing. Results show that all ML classifiers had areas under ROCs (AUC) ranging from 0.88 to 0.93, and performed significantly better than the expert-built Bayesian model. Missing clinical information marked as a value of missing (not missing at random) had a consistently improved performance among 3 (out of 4) ML classifiers when it was compared with the configuration of not assigning a value of missing (missing completely at random). The case/control ratios did not affect the classification performance given the large number of training cases. Our study demonstrates ED reports in conjunction with the use of ML and NLP with the handling of missing value information have a great potential for the detection of infectious diseases.
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