ArticleBMC medical informatics and decision making2021
Predicting COVID-19 disease progression and patient outcomes based on temporal deep learning.
Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Process Improvement Approaches for Increasing the Response of Emergency Departments against the COVID-19 Pandemic: A Systematic Review.International journal of environmental research and public health · 2021Pooled it
- A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data.Health data science · 2026Review
- Deep Learning Network Selection and Optimized Information Fusion for Enhanced COVID-19 Detection: A Literature Review.Diagnostics (Basel, Switzerland) · 2025Review
- Prospective study using artificial neural networks for identification of high-risk COVID-19 patients.Scientific reports · 2025Article
- Predictive modelling and identification of critical variables of mortality risk in COVID-19 patients.Scientific reports · 2025Article
- Use of machine learning models to predict mechanical ventilation, ECMO, and mortality in COVID-19.Frontiers in artificial intelligence · 2025Article
- Predicting SARS-CoV-2 infection among hemodialysis patients using deep neural network methods.Scientific reports · 2024Article
- Estimate the incubation period of coronavirus 2019 (COVID-19).Computers in biology and medicine · 2023Article
- Prognosis Prediction in COVID-19 Patients through Deep Feature Space Reasoning.Diagnostics (Basel, Switzerland) · 2023Article
- Myopia prediction for children and adolescents via time-aware deep learning.Scientific reports · 2023Article
- Adaptive model training strategy for continuous classification of time series.Applied intelligence (Dordrecht, Netherlands) · 2023Article
- Continuous diagnosis and prognosis by controlling the update process of deep neural networks.Patterns (New York, N.Y.) · 2023Article
- Article
- Machine learning models for predicting severe COVID-19 outcomes in hospitals.Informatics in medicine unlocked · 2023Article
- Patient allocation method in major epidemics under the situation of hierarchical diagnosis and treatment.BMC medical informatics and decision making · 2022Article
- Application of a Decision Tree Model to Predict the Outcome of Non-Intensive Inpatients Hospitalized for COVID-19.International journal of environmental research and public health · 2022Article
- Machine learning-based derivation and external validation of a tool to predict death and development of organ failure in hospitalized patients with COVID-19.Scientific reports · 2022Article
- Leveraging artificial intelligence and data science techniques in harmonizing, sharing, accessing and analyzing SARS-COV-2/COVID-19 data in Rwanda (LAISDAR Project): study design and rationale.BMC medical informatics and decision making · 2022Article
- Predicting the outcome for COVID-19 patients by applying time series classification to electronic health records.BMC medical informatics and decision making · 2022Observational
- A clinical case definition of post-COVID-19 condition by a Delphi consensus.The Lancet. Infectious diseases · 2022Review
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5 authors.
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Abstract
backgroundThe coronavirus disease 2019 (COVID-19) pandemic has caused health concerns worldwide since December 2019. From the beginning of infection, patients will progress through different symptom stages, such as fever, dyspnea or even death. Identifying disease progression and predicting patient outcome at an early stage helps target treatment and resource allocation. However, there is no clear COVID-19 stage definition, and few studies have addressed characterizing COVID-19 progression, making the need for this study evident.
methodsWe proposed a temporal deep learning method, based on a time-aware long short-term memory (T-LSTM) neural network and used an online open dataset, including blood samples of 485 patients from Wuhan, China, to train the model. Our method can grasp the dynamic relations in irregularly sampled time series, which is ignored by existing works. Specifically, our method predicted the outcome of COVID-19 patients by considering both the biomarkers and the irregular time intervals. Then, we used the patient representations, extracted from T-LSTM units, to subtype the patient stages and describe the disease progression of COVID-19.
resultsUsing our method, the accuracy of the outcome of prediction results was more than 90% at 12 days and 98, 95 and 93% at 3, 6, and 9 days, respectively. Most importantly, we found 4 stages of COVID-19 progression with different patient statuses and mortality risks. We ranked 40 biomarkers related to disease and gave the reference values of them for each stage. Top 5 is Lymph, LDH, hs-CRP, Indirect Bilirubin, Creatinine. Besides, we have found 3 complications - myocardial injury, liver function injury and renal function injury. Predicting which of the 4 stages the patient is currently in can help doctors better assess and cure the patient.
conclusionsTo combat the COVID-19 epidemic, this paper aims to help clinicians better assess and treat infected patients, provide relevant researchers with potential disease progression patterns, and enable more effective use of medical resources. Our method predicted patient outcomes with high accuracy and identified a four-stage disease progression. We hope that the obtained results and patterns will aid in fighting the disease.
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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.