ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
Severity Prediction for COVID-19 Patients via Recurrent Neural Networks.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Accurate treatment effect estimation using inverse probability of treatment weighting with deep learning.JAMIA open · 2025Article
- Predicting SARS-CoV-2 infection among hemodialysis patients using deep neural network methods.Scientific reports · 2024Article
- In-hospital real-time prediction of COVID-19 severity regardless of disease phase using electronic health records.PloS one · 2024Article
- Assessment of Prediction Tasks and Time Window Selection in Temporal Modeling of Electronic Health Record Data: a Systematic Review.Journal of healthcare informatics research · 2023Article
- Outbreak prediction of COVID-19 using Recurrent neural network with Gated Recurrent Units.Materials today. Proceedings · 2023Article
- Epidemiological challenges in pandemic coronavirus disease (COVID-19): Role of artificial intelligence.Wiley interdisciplinary reviews. Data mining and knowledge discoveryReview
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Authors and funding
5 authors.
Funding
Abstract
The novel coronavirus disease-2019 (COVID-19) pandemic has threatened the health of tens of millions of people worldwide and imposed heavy burden on global healthcare systems. In this paper, we propose a model to predict whether a patient infected with COVID-19 will develop severe outcomes based only on the patient's historical electronic health records (EHR) prior to hospital admission using recurrent neural networks. The model predicts risk score that represents the probability for a patient to progress into severe status (mechanical ventilation, tracheostomy, or death) after being infected with COVID-19. The model achieved 0.846 area under the receiver operating characteristic curve in predicting patients' outcomes averaged over 5-fold cross validation. While many of the existing models use features obtained after diagnosis of COVID-19, our proposed model only utilizes a patient's historical EHR to enable proactive risk management at the time of hospital admission.
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Identifiers
34457152PMC8378630What 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.