ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2016
Controlling testing volume for respiratory viruses using machine learning and text mining.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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.
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Who cites it
7 citing papers in PubMed, 16 citations in OpenAlex.
- Symptom-based diagnostic models for common respiratory viral infections: a machine learning and natural language processing study.Infectious Disease Modelling · 2026Article
- Development and validation of an explainable model for Mycoplasma pneumoniae infection in children.Scientific reports · 2026Article
- Tongue Image-Based Diagnosis of Acute Respiratory Tract Infection Using Machine Learning: Algorithm Development and Validation.JMIR medical informatics · 2025Article
- Restructured society and environment: A review on potential technological strategies to control the COVID-19 pandemic.The Science of the total environment · 2020Review
- Clinical Text Data in Machine Learning: Systematic Review.JMIR medical informatics · 2020Review
- Data analysis of COVID-2019 epidemic using machine learning methods: a case study of India.International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management · 2020Article
- On the Role of Artificial Intelligence in Genomics to Enhance Precision Medicine.Pharmacogenomics and personalized medicine · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Viral testing for pediatric inpatients with respiratory symptoms is common, with considerable associated charges. In an attempt to reduce testing volumes, we studied whether data available at the time of admission could aid in identifying children with low likelihood of having a particular viral origin of their symptoms, and thus safely forgo broad viral testing. We collected clinical data for 1,685 pediatric inpatients receiving respiratory virus testing from 2010-2012. Machine-learning on the data allowed us to construct pre-test models predicting whether a patient would test positive for a particular virus. Text mining improved the predictions for one viral test. Cost-sensitive models optimized for test sensitivity showed reasonable test specificities and an ability to reduce test volume by up to 46% for single viral tests. We conclude that diverse forms of data in the electronic medical record can be used productively to build models that help physicians reduce testing volumes.
Indexed as
Identifiers
28269950PMC5333257W2620596837What 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.