ArticleFrontiers in public health2020
Individual-Level Fatality Prediction of COVID-19 Patients Using AI Methods.
Article in Frontiers in public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Combating COVID-19 Crisis using Artificial Intelligence (AI) Based Approach: Systematic Review.Current topics in medicinal chemistry · 2024Pooled it
- Development of a Triage-Level Predictive Model for Hospitalization in the Emergency Department.Journal of clinical medicine · 2026Article
- Sample observed effects: enumeration, randomization and generalization.Scientific reports · 2025Article
- Deep learning in public health: Comparative predictive models for COVID-19 case forecasting.PloS one · 2024Article
- Evaluation of nutritional status and clinical depression classification using an explainable machine learning method.Frontiers in nutrition · 2023Article
- COVID-19 lockdowns and air quality: Evidence from grey spatiotemporal forecasts.Socio-economic planning sciences · 2022Article
- Deep-Risk: Deep Learning-Based Mortality Risk Predictive Models for COVID-19.Diagnostics (Basel, Switzerland) · 2022Article
- Body Language Analysis in Healthcare: An Overview.Healthcare (Basel, Switzerland) · 2022Article
- Applications of Bayesian shrinkage prior models in clinical research with categorical responses.BMC medical research methodology · 2022Article
- COVID Mortality Prediction with Machine Learning Methods: A Systematic Review and Critical Appraisal.Journal of personalized medicine · 2021Review
- Article
- COVID-19 prediction models: a systematic literature review.Osong public health and research perspectives · 2021Article
- Prediction and Feature Importance Analysis for Severity of COVID-19 in South Korea Using Artificial Intelligence: Model Development and Validation.Journal of medical Internet research · 2021Article
- Using AI ethically to tackle covid-19.BMJ (Clinical research ed.) · 2021Article
- A novel deep learning-based quantification of serial chest computed tomography in Coronavirus Disease 2019 (COVID-19).Scientific reports · 2021Article
- Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review.Computational and structural biotechnology journal · 2021Review
- Machine learning approaches in COVID-19 diagnosis, mortality, and severity risk prediction: A review.Informatics in medicine unlocked · 2021Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The global covid-19 pandemic puts great pressure on medical resources worldwide and leads healthcare professionals to question which individuals are in imminent need of care. With appropriate data of each patient, hospitals can heuristically predict whether or not a patient requires immediate care. We adopted a deep learning model to predict fatality of individuals tested positive given the patient's underlying health conditions, age, sex, and other factors. As the allocation of resources toward a vulnerable patient could mean the difference between life and death, a fatality prediction model serves as a valuable tool to healthcare workers in prioritizing resources and hospital space. The models adopted were evaluated and refined using the metrics of accuracy, specificity, and sensitivity. After data preprocessing and training, our model is able to predict whether a covid-19 confirmed patient is likely to be dead or not, given their information and disposition. The metrics between the different models are compared. Results indicate that the deep learning model outperforms other machine learning models to solve this rare event prediction problem.
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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.