Deep Learning Models for Predicting Severe Progression in COVID-19-Infected Patients: Retrospective Study

Ho, Thao Thi; Park, Jong-Min; Kim, Taewoo; Park, Byung-Geon; Lee, Jaehee; Kim, Jin Young; Kim, Ki Beom; Choi, Sooyoung; Kim, Young Hwan; Lim, Jae‐Kwang; Choi, Seock Hwan

doi:10.2196/24973

Cited by 30 publications

(29 citation statements)

References 33 publications

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“…In the correlation heatmap of clinical and laboratory features, CRP and WBC had a strong positive correlation with the endpoint, age was described as significant risk factor related to the endpoint; oxygen saturation and female sex were negatively correlated with the endpoint. This mixed ACNN model obtained a high performance with an AUC of 0.916, accuracy of 93.9% and specificity of 96.9% [102].…”

Section: Ai In the Stratification And Definition Of Severity And Complications Of Covid-19 Pneumonia At Chest Ctmentioning

confidence: 87%

The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review

et al. 2021

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Diagnostic imaging is regarded as fundamental in the clinical work-up of patients with a suspected or confirmed COVID-19 infection. Recent progress has been made in diagnostic imaging with the integration of artificial intelligence (AI) and machine learning (ML) algorisms leading to an increase in the accuracy of exam interpretation and to the extraction of prognostic information useful in the decision-making process. Considering the ever expanding imaging data generated amid this pandemic, COVID-19 has catalyzed the rapid expansion in the application of AI to combat disease. In this context, many recent studies have explored the role of AI in each of the presumed applications for COVID-19 infection chest imaging, suggesting that implementing AI applications for chest imaging can be a great asset for fast and precise disease screening, identification and characterization. However, various biases should be overcome in the development of further ML-based algorithms to give them sufficient robustness and reproducibility for their integration into clinical practice. As a result, in this literature review, we will focus on the application of AI in chest imaging, in particular, deep learning, radiomics and advanced imaging as quantitative CT.

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Section: Ai In the Stratification And Definition Of Severity And Complications Of Covid-19 Pneumonia At Chest Ctmentioning

confidence: 87%

The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review

et al. 2021

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“…In fact, many of the medical imaging studies that integrated heterogenous data have shown that data integration led to AI models with better performance compared to models built with imaging data alone. 53–55 , 62 , 65 , 69 , 76–78 Furthermore, although some data are difficult to get due to privacy issues or simply being unavailable, there are still a range of public data on risk factors that could be easily obtained for modeling. Many studies we reviewed leveraged the “free” data sources, such as the huge amounts of environmental data from the National Oceanic and Atmospheric Administration or the socioeconomic data from the Census Bureau.…”

Section: Discussionmentioning

confidence: 99%

The application of artificial intelligence and data integration in COVID-19 studies: a scoping review

Guo

Zhang

Lyu

et al. 2021

Journal of the American Medical Informatics Association

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Objective To summarize how artificial intelligence (AI) is being applied in COVID-19 research and determine whether these AI applications integrated heterogenous data from different sources for modeling. Materials and Methods We searched 2 major COVID-19 literature databases, the National Institutes of Health’s LitCovid and the World Health Organization’s COVID-19 database on March 9, 2021. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline, 2 reviewers independently reviewed all the articles in 2 rounds of screening. Results In the 794 studies included in the final qualitative analysis, we identified 7 key COVID-19 research areas in which AI was applied, including disease forecasting, medical imaging-based diagnosis and prognosis, early detection and prognosis (non-imaging), drug repurposing and early drug discovery, social media data analysis, genomic, transcriptomic, and proteomic data analysis, and other COVID-19 research topics. We also found that there was a lack of heterogenous data integration in these AI applications. Discussion Risk factors relevant to COVID-19 outcomes exist in heterogeneous data sources, including electronic health records, surveillance systems, sociodemographic datasets, and many more. However, most AI applications in COVID-19 research adopted a single-sourced approach that could omit important risk factors and thus lead to biased algorithms. Integrating heterogeneous data for modeling will help realize the full potential of AI algorithms, improve precision, and reduce bias. Conclusion There is a lack of data integration in the AI applications in COVID-19 research and a need for a multilevel AI framework that supports the analysis of heterogeneous data from different sources.

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“…They could predict the mortality on days three, five, and seven after hospitalization with an AUC of 0.89, 0.85, and 0.84, respectively. In another AI model by a convolutional neural network (CNN), researchers differentiated the progressive form of infection versus the non-progressive form using CT images and clinical data with an AUC of 0.91 [ 12 ].…”

Section: Discussionmentioning

confidence: 99%

Accuracy of Machine Learning Models to Predict Mortality in COVID-19 Infection Using the Clinical and Laboratory Data at the Time of Admission

Tabatabaie¹,

Sarrami²,

Didehdar³

et al. 2021

Cureus

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This study aimed to develop a predictive model to predict patients' mortality with coronavirus disease 2019 from the basic medical data on the first day of admission. MethodsThe medical data including the demographic, clinical, and laboratory features on the first day of admission of clinically diagnosed COVID-19 patients were documented. The outcome of patients was also recorded as discharge or death. Feature selection models were then implemented and different machine learning models were developed on top of the selected features to predict discharge or death. The trained models were then tested on the test dataset. ResultsA total of 520 patients were included in the training dataset. The feature selection demonstrated 22 features as the most powerful predictive features. Among different machine learning models, the naive Bayes demonstrated the best performance with an area under the curve of 0.85. The ensemble model of the naive Bayes and neural network combination had slightly better performance with an area under the curve of 0.86. The models had relatively the same performance on the test dataset. ConclusionDeveloping a predictive machine learning model based on the basic medical features on the first day of admission in COVID-19 infection is feasible with acceptable performance.

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Deep Learning Models for Predicting Severe Progression in COVID-19-Infected Patients: Retrospective Study

Cited by 30 publications

References 33 publications

The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review

The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review

The application of artificial intelligence and data integration in COVID-19 studies: a scoping review

Accuracy of Machine Learning Models to Predict Mortality in COVID-19 Infection Using the Clinical and Laboratory Data at the Time of Admission

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