ArticleJournal of the American Medical Informatics Association : JAMIA2020
An artificial intelligence approach to COVID-19 infection risk assessment in virtual visits: A case report.
Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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
29 citing papers in PubMed.
- Predictive triage for testing may improve control of a COVID-19 epidemic while reducing testing requirements.Archives of public health = Archives belges de sante publique · 2026Article
- Target identification of natural products in cancer with chemical proteomics and artificial intelligence approaches.Cancer biology & medicine · 2025Review
- Machine learning applications in risk management: Trends and research agenda.F1000Research · 2025Review
- Real world outcomes of distributing Lucira Check-It® COVID self-tests in Ontario, Canada: the GetaKit COVID study.BMC public health · 2024Observational
- Foundational domains and competencies for baccalaureate health informatics education.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review.Cureus · 2023Review
- Machine learning approaches for electronic health records phenotyping: a methodical review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- COVID-19 advising application development for Apple devices (iOS).PeerJ. Computer science · 2023Article
- The Role of Natural Language Processing during the COVID-19 Pandemic: Health Applications, Opportunities, and Challenges.Healthcare (Basel, Switzerland) · 2022Review
- A rapid review of machine learning approaches for telemedicine in the scope of COVID-19.Artificial intelligence in medicine · 2022Review
- Monitoring COVID-19 on Social Media: Development of an End-to-End Natural Language Processing Pipeline Using a Novel Triage and Diagnosis Approach.Journal of medical Internet research · 2022Article
- Artificial intelligence for COVID-19: battling the pandemic with computational intelligence.Intelligent medicine · 2022Review
- Contribution of Deep-Learning Techniques Toward Fighting COVID-19: A Bibliometric Analysis of Scholarly Production During 2020.IEEE access : practical innovations, open solutions · 2022Article
- A Natural Language Processing (NLP) Evaluation on COVID-19 Rumour Dataset Using Deep Learning Techniques.Computational intelligence and neuroscience · 2022Article
- Natural language processing enabling COVID-19 predictive analytics to support data-driven patient advising and pooled testing.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review.Expert systems with applications · 2021Review
- Overview of Technologies Implemented During the First Wave of the COVID-19 Pandemic: Scoping Review.Journal of medical Internet research · 2021Article
- How artificial intelligence may help the Covid-19 pandemic: Pitfalls and lessons for the future.Reviews in medical virology · 2021Review
- Managing Pandemic Responses with Health Informatics - Challenges for Assessing Digital Health Technologies.Yearbook of medical informatics · 2021Review
- The Clinical Information Systems Response to the COVID-19 Pandemic.Yearbook of medical informatics · 2021Review
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
objectiveIn an effort to improve the efficiency of computer algorithms applied to screening for coronavirus disease 2019 (COVID-19) testing, we used natural language processing and artificial intelligence-based methods with unstructured patient data collected through telehealth visits. MATERIALS AND
methodsAfter segmenting and parsing documents, we conducted analysis of overrepresented words in patient symptoms. We then developed a word embedding-based convolutional neural network for predicting COVID-19 test results based on patients' self-reported symptoms.
resultsText analytics revealed that concepts such as smell and taste were more prevalent than expected in patients testing positive. As a result, screening algorithms were adapted to include these symptoms. The deep learning model yielded an area under the receiver-operating characteristic curve of 0.729 for predicting positive results and was subsequently applied to prioritize testing appointment scheduling.
conclusionsInformatics tools such as natural language processing and artificial intelligence methods can have significant clinical impacts when applied to data streams early in the development of clinical systems for outbreak response.
Indexed as
Identifiers
What 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.