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

Abstract

BACKGROUND Many COVID-19 patients rapidly progress into respiratory failure with a broad range of severity. Identification of the high-risk cases is critical for early intervention. OBJECTIVE The aim of this study is to develop deep learning models that can rapidly diagnose high-risk COVID-19 patients based on computed tomography (CT) images and clinical data. METHODS We analyzed 297 COVID-19 patients from five hospitals in Daegu, South Korea. A mixed model (ACNN) including an artificial neural network for clinical data and a convolution-neural network for 3D CT imaging data is developed to classify high-risk cases with a severe progression (event) from low-risk COVID-19 patients (event-free). RESULTS By using the mixed ACNN model, we could obtain high classification performance using novel coronavirus pneumonia (NCP) lesion images (93.9% accuracy, 80.8% sensitivity, 96.9% specificity, and 0.916 AUC) and using lung segmentation images (94.3% accuracy, 74.7% sensitivity, 95.9% specificity, and 0.928 AUC) for event vs. event-free groups. CONCLUSIONS Our study has successfully differentiated high-risk cases among COVID-19 patients using the imaging and clinical features of COVID-19 patients. The developed model is potentially utilized as a prediction tool for intervening active therapy.

Do age, gender, and subjective health-related factors influence health-related life satisfaction in people with disabilities who are physically active?: a secondary analysis

“…Depression is associated with lower life satisfaction among individuals with disabilities [18]. Specifically, those with disabilities who experienced depression as a result of COVID-19 were found to have a 1.6-fold higher risk of reduced life satisfaction than individuals without disabilities [19].…”

“…In summary, prior research has established that the health and life satisfaction of individuals with disabilities is influenced by factors related to disability [2], sociodemographics [7,[21][22][23], and health [8,9,[17][18][19][20]. However, the majority of these studies have examined the impact of each factor in isolation and have…”