Comparison of machine learning classifiers for influenza detection from emergency department free-text reports
Open accessAbstract
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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Many reviews examine the strengths and weaknesses of different machine learning algorithms on single-omics and multi-omics datasets.
“In the literature, many reviews cover the key strengths and weaknesses of different ML algorithms using single omics (Amancio et al., 2014;López Pineda et al., 2015;Sakr et al., 2017;Uddin et al., 2019) and multi-omics (Ma et al., 2016;Francescatto et al., 2018;J. Xu et al., 2019;Sathyanarayanan et al., 2020) datasets.”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as López Pineda et al., 2015
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Machine Learning and Syncope Management in the ED: The Future Is Coming
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Artificial Intelligence system to support the clinical decision for influenza
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COVIDAL: A Machine Learning Classifier for Digital COVID-19 Diagnosis in German Hospitals
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Implementation of Artificial Intelligence in Disease Prediction and Healthcare System- A Survey
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Cheminformatics and Bioinformatics at the Interface with Systems Biology
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Population-aware hierarchical bayesian domain adaptation via multi-component invariant learning
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Swine flu Detection and Location using Machine Learning Techniques and GIS
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Early Detection of Influenza Using Machine Learning Techniques
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Machine Learning Approaches for Infectious Disease Surveillance During Emergencies
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A novel LSTM-based multimodal system for robust diagnosis of respiratory infectious diseases
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- Authors
- López Pineda, A., Ye, Y., Visweswaran, S., Cooper, G. F., Wagner, M. M., & Tsui, F. (.
- Affiliations
- University of Pittsburgh (US)
- Corresponding author
- Tsui, Fuchiang (Rich)
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2015-12
- Volume
- 58
- Pages
- 60-69
- ISSN
- 1532-0464 (ISSN-L)
- PMC
- PMC4684714
- OpenAlex
- W1927503436
- Cited by
- 88
- References
- 53
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 8 · 2023: 11 · 2022: 15 · 2021: 11 · 2020: 11 · 2019: 11 · 2018: 7 · 2017: 4 · 2016: 4 · 2015: 1
- FWCI
- 4.19
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation (0911032)
- Pennsylvania Department of Health
- University of Pittsburgh
- Centers for Disease Control and Prevention (U38HK000063, P01HK000086)
- U.S. National Library of Medicine (R01LM012095, R01LM010020, R01LM011370)
- Center for Advanced Study, University of Illinois at Urbana-Champaign
- Topics
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- influenza virus detection, machine learning classifiers, natural language processing, topaz, biosurveillance, infectious disease detection, Bayesian classifier, electronic health records
- MeSH terms
- Machine Learning, Emergency Service, Hospital, Humans, Influenza, Human
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