Severity Prediction for COVID-19 Patients via Recurrent Neural Networks

Lee, Junghwan; Ta, Casey; Kim, Jae Hyun; Liu, Cong; Weng, Chunhua

doi:10.1101/2020.08.28.20184200

Cited by 5 publications

(3 citation statements)

References 21 publications

(27 reference statements)

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“…Recently, a dynamic prediction model for forecasting future COVID-19-related aggravation [ 16 ] or mortality [ 11 ] was developed. Although temporal model (RNN) improved the performance of numerous tasks [ 13 , 27 , 28 ], daily severity prediction tasks did not benefit from the use of temporal model in this study. We anticipated improved performance by incorporating temporal information into the transformer model, but the improvement was task-dependent.…”

Section: Discussionmentioning

confidence: 77%

In-hospital real-time prediction of COVID-19 severity regardless of disease phase using electronic health records

Park,

Choi,

Kim

et al. 2024

PLoS ONE

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Coronavirus disease 2019 (COVID-19) has strained healthcare systems worldwide. Predicting COVID-19 severity could optimize resource allocation, like oxygen devices and intensive care. If machine learning model could forecast the severity of COVID-19 patients, hospital resource allocation would be more comfortable. This study evaluated machine learning models using electronic records from 3,996 COVID-19 patients to forecast mild, moderate, or severe disease up to 2 days in advance. A deep neural network (DNN) model achieved 91.8% accuracy, 0.96 AUROC, and 0.90 AUPRC for 2-day predictions, regardless of disease phase. Tree-based models like random forest achieved slightly better metrics (random forest: 94.1% of accuracy, 0.98 AUROC, 0.95 AUPRC; Gradient boost: 94.1% of accuracy, 0.98 AUROC, 0.94 AUPRC), prioritizing treatment factors like steroid use. However, the DNN relied more on fixed patient factors like demographics and symptoms in aspect to SHAP value importance. Since treatment patterns vary between hospitals, the DNN may be more generalizable than tree-based models (random forest, gradient boost model). The results demonstrate accurate short-term forecasting of COVID-19 severity using routine clinical data. DNN models may balance predictive performance and generalizability better than other methods. Severity predictions by machine learning model could facilitate resource planning, like ICU arrangement and oxygen devices.

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Section: Discussionmentioning

confidence: 77%

In-hospital real-time prediction of COVID-19 severity regardless of disease phase using electronic health records

Park,

Choi,

Kim

et al. 2024

PLoS ONE

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“…To exploit the data set for several predictive tasks, recurrent neural network (RNN)‐based framework can be the most suitable choice. For example, a group of scientists has incorporated RNN‐based framework (Lee et al, 2021 ) on the electronic health record (EHR) data set to anticipate the severity of the disease concerning ventilation, tracheostomy, or death of a COVID‐19‐infected patient. The foremost benefit of the technique is that a medical practitioner can predict the future hazard without any immediate pathological parameters.…”

Section: Introductionmentioning

confidence: 99%

Epidemiological challenges in pandemic coronavirus disease (COVID‐19): Role of artificial intelligence

Dasgupta

Bakshi

Mukherjee

et al. 2022

WIREs Data Min & Knowl

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World is now experiencing a major health calamity due to the coronavirus disease (COVID‐19) pandemic, caused by the severe acute respiratory syndrome coronavirus clade 2. The foremost challenge facing the scientific community is to explore the growth and transmission capability of the virus. Use of artificial intelligence (AI), such as deep learning, in (i) rapid disease detection from x‐ray or computed tomography (CT) or high‐resolution CT (HRCT) images, (ii) accurate prediction of the epidemic patterns and their saturation throughout the globe, (iii) forecasting the disease and psychological impact on the population from social networking data, and (iv) prediction of drug–protein interactions for repurposing the drugs, has attracted much attention. In the present study, we describe the role of various AI‐based technologies for rapid and efficient detection from CT images complementing quantitative real‐time polymerase chain reaction and immunodiagnostic assays. AI‐based technologies to anticipate the current pandemic pattern, prevent the spread of disease, and face mask detection are also discussed. We inspect how the virus transmits depending on different factors. We investigate the deep learning technique to assess the affinity of the most probable drugs to treat COVID‐19. This article is categorized under: Application Areas > Health Care Algorithmic Development > Biological Data Mining Technologies > Machine Learning

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“…As a type of machine learning where models contain multiple layers of neural networks[5], deep learning has advanced many scientific fields such as computer vision, speech recognition and natural language processing[5]. It has also been applied to various healthcare tasks[6, 7], including disease diagnosis[8, 9], disease onset prediction[10], mortality prediction[11], length of stay in hospital prediction[12], phenotyping[13], and novel drug discovery[14]. Given these promising applications, there has been great interest in using deep learning to address the challenges for rare diseases.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Rare Disease: A Scoping Review

Lee

Liu

Kim

et al. 2022

Preprint

Self Cite

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Although individually rare, collectively more than 7,000 rare diseases affect about 10% of patients. Each of the rare diseases impacts the quality of life for patients and their families, and incurs significant societal costs. The low prevalence of each rare disease causes formidable challenges in accurately diagnosing and caring for these patients and engaging participants in research to advance treatments. Deep learning has advanced many scientific fields and has been applied to many healthcare tasks. This study reviewed the current uses of deep learning to advance rare disease research. Among the 332 reviewed articles, we found that deep learning has been actively used for rare neoplastic diseases (250/332), followed by rare genetic diseases (170/332) and rare neurological diseases (127/332). Convolutional neural networks (307/332) were the most frequently used deep learning architecture, presumably because image data were the most commonly available data type in rare disease research. Diagnosis is the main focus of rare disease research using deep learning (263/332). We summarized the challenges and future research directions for leveraging deep learning to advance rare disease research.

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Severity Prediction for COVID-19 Patients via Recurrent Neural Networks

Cited by 5 publications

References 21 publications

In-hospital real-time prediction of COVID-19 severity regardless of disease phase using electronic health records

In-hospital real-time prediction of COVID-19 severity regardless of disease phase using electronic health records

Epidemiological challenges in pandemic coronavirus disease (COVID‐19): Role of artificial intelligence

Deep Learning for Rare Disease: A Scoping Review

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