ArticleFrontiers in public health2021
Recurrent Neural Network and Reinforcement Learning Model for COVID-19 Prediction.
Article in Frontiers in public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advanced applications in chronic disease monitoring using IoT mobile sensing device data, machine learning algorithms and frame theory: a systematic review.Frontiers in public health · 2025Pooled it
- Development and Validation of a Competitive Risk Model in Elderly Patients With Chromophobe Cell Renal Carcinoma: A Population-Based Study.Frontiers in public health · 2022Trial
- A Multi-Agent Reinforcement Learning Framework for Public Health Decision Analysis.Healthcare analytics (New York, N.Y.) · 2025Article
- An optimization protocol for MRI examination resource allocation based on demand forecasting and linear programming.Scientific reports · 2025Article
- Use of machine learning models to predict mechanical ventilation, ECMO, and mortality in COVID-19.Frontiers in artificial intelligence · 2025Article
- An Integrated Framework for Infectious Disease Control Using Mathematical Modeling and Deep Learning.IEEE open journal of engineering in medicine and biology · 2025Article
- Rapid Detection of Soil Available Phosphorus using Capacitively Coupled Contactless Conductivity Detection.Current organic synthesis · 2025Article
- Innovative applications of artificial intelligence during the COVID-19 pandemic.Infectious medicine · 2024Review
- Deep learning in public health: Comparative predictive models for COVID-19 case forecasting.PloS one · 2024Article
- Optimal adaptive nonpharmaceutical interventions to mitigate the outbreak of respiratory infections following the COVID-19 pandemic: a deep reinforcement learning study in Hong Kong, China.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Article
- Predicting medical device failure: a promise to reduce healthcare facilities cost through smart healthcare management.PeerJ. Computer science · 2023Article
- A Hybrid Model for Coronavirus Disease 2019 Forecasting Based on Ensemble Empirical Mode Decomposition and Deep Learning.International journal of environmental research and public health · 2022Article
- Daily surgery caseload prediction: towards improving operating theatre efficiency.BMC medical informatics and decision making · 2022Article
- FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information.Sensors (Basel, Switzerland) · 2022Article
- Article
- Quantifying the dynamic transmission of COVID-19 asymptomatic and symptomatic infections: Evidence from four Chinese regions.Frontiers in public health · 2022Article
- Development and Validation of a Nomogram to Predict Cancer-Specific Survival for Middle-Aged Patients With Early-Stage Hepatocellular Carcinoma.Frontiers in public health · 2022Article
- Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models.Journal of healthcare engineering · 2022Article
- Machine Learning-Based Prediction Models for Depression Symptoms Among Chinese Healthcare Workers During the Early COVID-19 Outbreak in 2020: A Cross-Sectional Study.Frontiers in psychiatry · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Detection and prediction of the novel Coronavirus present new challenges for the medical research community due to its widespread across the globe. Methods driven by Artificial Intelligence can help predict specific parameters, hazards, and outcomes of such a pandemic. Recently, deep learning-based approaches have proven a novel opportunity to determine various difficulties in prediction. In this work, two learning algorithms, namely deep learning and reinforcement learning, were developed to forecast COVID-19. This article constructs a model using Recurrent Neural Networks (RNN), particularly the Modified Long Short-Term Memory (MLSTM) model, to forecast the count of newly affected individuals, losses, and cures in the following few days. This study also suggests deep learning reinforcement to optimize COVID-19's predictive outcome based on symptoms. Real-world data was utilized to analyze the success of the suggested system. The findings show that the established approach promises prognosticating outcomes concerning the current COVID-19 pandemic and outperformed the Long Short-Term Memory (LSTM) model and the Machine Learning model, Logistic Regresion (LR) in terms of error rate.
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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.