Predicting Prolonged Hospitalization and Supplemental Oxygenation in Patients with COVID-19 Infection from Ambulatory Chest Radiographs using Deep Learning

Pyrros, Ayis; Flanders, Adam E.; Rodríguez-Fernández, Jorge Mario; Chen, Andrew; Cole, Patrick; Wenzke, Daniel R.; Hart, Eric; Harford, Samuel; Horowitz, Jeanne M.; Nikolaidis, Paul; Muzaffar, Nadir; Boddipalli, Viveka; Nebhrajani, Jai; Siddiqui, Nasir; Willis, Melinda; Darabi, Houshang; Koyejo, Oluwasanmi; Galanter, William L.

doi:10.1016/j.acra.2021.05.002

Cited by 9 publications

(4 citation statements)

References 29 publications

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“…We successfully created models and compared our results with other published models. These models were published in journals [9][10][11][12] and presented at national conferences. However, translation of such models to the clinical setting proved difficult, partly due to the malalignment of incentives throughout our hospital organization.…”

Section: Theme 4: Model Buildingmentioning

confidence: 99%

“…In addition, we generated multiple predictive models, incorporated machine learning, leveraged natural language processing (NLP), and performed imaging analytics. Our work has been published in several peer-reviewed journals [9][10][11][12][13]. New goals and aims were developed throughout the project, including examining social determinants of health and comparing our findings with larger national and regional COVID-19 patient datasets (e.g., NC3 [14]).…”

Section: Introductionmentioning

confidence: 99%

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Lessons learned: Development of COVID-19 clinical staging models at a large urban research institution

Huang¹,

Chaisson²,

Galanter³

et al. 2023

J. Clin. Trans. Sci.

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Section: Theme 4: Model Buildingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Lessons learned: Development of COVID-19 clinical staging models at a large urban research institution

Huang¹,

Chaisson²,

Galanter³

et al. 2023

J. Clin. Trans. Sci.

Self Cite

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“…However, a DL model has not been fully described in detail using clinical information and chest images together [ 24 ]. It has been recently reported that a DL model using x-rays predicts the presence or absence of oxygen supplementation, a predictor of hospitalization and delayed discharge, which is associated with disease severity [ 25 ]. Thus, we considered a DL model that combined clinical and CT imaging findings to predict oxygen supplementation in an early stage.…”

Section: Introductionmentioning

confidence: 99%

Prediction of oxygen supplementation by a deep-learning model integrating clinical parameters and chest CT images in COVID-19

et al. 2023

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Purpose As of March 2023, the number of patients with COVID-19 worldwide is declining, but the early diagnosis of patients requiring inpatient treatment and the appropriate allocation of limited healthcare resources remain unresolved issues. In this study we constructed a deep-learning (DL) model to predict the need for oxygen supplementation using clinical information and chest CT images of patients with COVID-19. Materials and methods We retrospectively enrolled 738 patients with COVID-19 for whom clinical information (patient background, clinical symptoms, and blood test findings) was available and chest CT imaging was performed. The initial data set was divided into 591 training and 147 evaluation data. We developed a DL model that predicted oxygen supplementation by integrating clinical information and CT images. The model was validated at two other facilities (n = 191 and n = 230). In addition, the importance of clinical information for prediction was assessed. Results The proposed DL model showed an area under the curve (AUC) of 89.9% for predicting oxygen supplementation. Validation from the two other facilities showed an AUC > 80%. With respect to interpretation of the model, the contribution of dyspnea and the lactate dehydrogenase level was higher in the model. Conclusions The DL model integrating clinical information and chest CT images had high predictive accuracy. DL-based prediction of disease severity might be helpful in the clinical management of patients with COVID-19.

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“…is paper also discusses the challenges and expected future trends in the application of deep learning to heart sounds classification with the objective of providing an essential reference for further study [22]. Supervised multitask deep learning with convolutional neural networks (CNNs) on frontal chest radiographs was able to predict many underlying patient comorbidities represented by hierarchical condition categories (HCCs) from the International Classification of Diseases, Tenth Revision, including those corresponding to diabetes with chronic complications, morbid obesity, congestive heart failure, cardiac arrhythmias, and chronic obstructive pulmonary disease [23].…”

Section: Introductionmentioning

confidence: 99%

Research on Prediction of Physical Fitness Test Results in Colleges and Universities Based on Deep Learning

Wang¹,

Wu²,

Jiang

et al. 2022

Mathematical Problems in Engineering

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All-round development strategy of quality education makes primary and secondary school students not only pursue the improvement of achievement but also carry out physical exercise. Physical training is the material basis for students to study other disciplines, and the core is to improve students’ own physical quality and increase their physique. Having a strong body helps students have certain physical strength to study in other courses. In recent years, in the background of the scientific era, college students in China obviously have some problems, such as insufficient awareness of physical exercise and serious decline in physical fitness. Nowadays, teenagers are addicted to games and go out to become members of the low-headed people. Nowadays, it is very unhealthy for teenagers to go out with their mobile phones as “low-headed people.” In order to avoid college students getting rid of this living condition, colleges and universities carry out physical fitness tests every year to promote contemporary college students to strengthen exercise. College students, as the main force in the future construction of the motherland, should not only master professional knowledge but also improve their physical fitness. Good health is the greatest capital in one’s life. Every year, some students fail to pass the physical fitness test in universities. It stands to reason that college students are in the age of high youth, and physical fitness test should be a piece of cake for them. In the face of the inconsistency between the predicted results and the actual results, this paper analyzes this. Based on the above situation, With the aim of improving students’ training efficiency and physical performance, the physical performance prediction model of deep learning is designed and analyzed to predict the performance, analyze the influencing factors of the model and how to reduce the influencing components of the factors, and analyze and compare the performance of various prediction models to find out the best model, so as to make the predicted value closer to the true value.

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Predicting Prolonged Hospitalization and Supplemental Oxygenation in Patients with COVID-19 Infection from Ambulatory Chest Radiographs using Deep Learning

Cited by 9 publications

References 29 publications

Lessons learned: Development of COVID-19 clinical staging models at a large urban research institution

Lessons learned: Development of COVID-19 clinical staging models at a large urban research institution

Prediction of oxygen supplementation by a deep-learning model integrating clinical parameters and chest CT images in COVID-19

Research on Prediction of Physical Fitness Test Results in Colleges and Universities Based on Deep Learning

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