Centers for Disease Control and Prevention. CDC covid data tracker. https://covid.cdc.gov/covid-data-tracker/ (Accessed 13 June 2022) (2022).
Karim, S. S. A. & Karim, Q. A. Omicron sars-cov-2 variant: A new chapter in the covid-19 pandemic. Lancet 398(10317), 2126–2128 (2021).
Google Scholar
Kupferschmidt, K. & Wadman, M. Delta variant triggers new phase in the pandemic. Science 372(6549), 1375–1376 (2021).
Google Scholar
McCue, C. et al. Long term outcomes of critically ill covid-19 pneumonia patients: Early learning. Intensive Care Med. 47(2), 240–241 (2021).
Google Scholar
Michelen, M. et al. Characterising long term covid-19: A living systematic review. BMJ Glob. Health 6(9), e005427 (2021).
Google Scholar
Jacobi, A. et al. Portable chest x-ray in coronavirus disease-19 (covid-19): A pictorial review. Clin. Imaging 64, 35–42 (2020).
Google Scholar
Kim, H. W. et al. The role of initial chest x-ray in triaging patients with suspected covid-19 during the pandemic. Emerg. Radiol. 27(6), 617–621 (2020).
Google Scholar
Akl, E. A. et al. Use of chest imaging in the diagnosis and management of covid-19: A who rapid advice guide. Radiology 298(2), E63–E69 (2021).
Google Scholar
Borkowski, A. A. et al. Using artificial intelligence for covid-19 chest x-ray diagnosis. Fed. Pract. 37(9), 398–404 (2020).
Google Scholar
Balbi, M. et al. Chest x-ray for predicting mortality and the need for ventilatory support in covid-19 patients presenting to the emergency department. Eur. Radiol. 31(4), 1999–2012 (2021).
Google Scholar
Maroldi, R. et al. Which role for chest x-ray score in predicting the outcome in covid-19 pneumonia?. Eur. Radiol. 31(6), 4016–4022 (2021).
Google Scholar
Monaco, C. G. et al. Chest x-ray severity score in covid-19 patients on emergency department admission: A two-centre study. Eur. Radiol. Exp. 4(1), 68 (2020).
Google Scholar
Hussain, L. et al. Machine-learning classification of texture features of portable chest x-ray accurately classifies covid-19 lung infection. Biomed. Eng. Online 19(1), 88 (2020).
Google Scholar
Ismael, A. M. & Şengür, A. Deep learning approaches for covid-19 detection based on chest x-ray images. Expert Syst. Appl. 164(114), 054 (2021).
Salvatore, M. et al. A phenome-wide association study (phewas) of covid-19 outcomes by race using the electronic health records data in michigan medicine. J. Clin. Med. 10(7), 1351 (2021).
Google Scholar
Spector-Bagdady, K. et al. Coronavirus disease 2019 (covid-19) clinical trial oversight at a major academic medical center: Approach of michigan medicine. Clin. Infect. Dis. 71(16), 2187–2190 (2020).
Google Scholar
Nypaver, M. et al. The michigan emergency department improvement collaborative: A novel model for implementing large scale practice change in pediatric emergency care. Pediatrics 142(1 MeetingAbstract), 105 (2018).
Google Scholar
Abbas, A., Abdelsamea, M. M. & Gaber, M. M. Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. Appl. Intell. 51, 854–864 (2021).
Google Scholar
Gupta, A. et al. Association between antecedent statin use and decreased mortality in hospitalized patients with COVID-19. Nat. Commun. 12(1), 1325 (2021).
Google Scholar
Cox, D. R. Regression models and life tables (with discussion). J. R. Stat. Soc. B 34(2), 187–220 (1972).
Google Scholar
Therneau, T. M. & Grambsch, P. M. Modeling survival data: Extending the Cox model. In The Cox Model 39–77 (Springer, 2000).
Google Scholar
Pölsterl, S., Navab, N. & Katouzian, A. An efficient training algorithm for kernel survival support vector machines. https://doi.org/10.48550/arXiv.1611.07054 (Preprint posted online November 21, 2016).
Ishwaran, H. et al. Random survival forests. Ann. Appl. Stat. 2(3), 841–860 (2008).
Google Scholar
Hothorn, T. et al. Survival ensembles. Biostatistics 7(3), 355–373 (2006).
Google Scholar
Zhou, Z. H. Ensemble Methods: Foundations and Algorithms (CRC Press, 2012).
Google Scholar
Zwanenburg, A. et al. Image biomarker standardisation initiative. https://doi.org/10.48550/arXiv.1612.07003 (Preprint posted online December 21, 2016)
Harrell, F. E. et al. Evaluating the yield of medical tests. JAMA 247(18), 2543–2546 (1982).
Google Scholar
Harrell, F. E. Jr., Lee, K. L. & Mark, D. B. Multivariable prognostic models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat. Med. 15(4), 361–387 (1996).
Google Scholar
Holste, G. et al. End-to-end learning of fused image and non-image features for improved breast cancer classification from mri. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 3294–3303 (2021).
Zhou, H. et al. Diagnosis of distant metastasis of lung cancer: Based on clinical and radiomic features. Transl. Oncol. 11(1), 31–36 (2018).
Google Scholar
Militello, C. et al. CT Radiomic Features and Clinical Biomarkers for Predicting Coronary Artery Disease. Cogn. Comput. 15(1), 238–253 (2023).
Google Scholar
Huang, S. C. et al. Multimodal fusion with deep neural networks for leveraging CT imaging and electronic health record: A case-study in pulmonary embolism detection. Sci. Rep. 10(1), 1–9 (2020).
Google Scholar
Liu, Z. et al. Imaging genomics for accurate diagnosis and treatment of tumors: A cutting edge overview. Biomed. Pharmacother. 135, 111173 (2021).
Google Scholar
Tomaszewski, M. R. & Gillies, R. J. The biological meaning of radiomic features. Radiology 298(3), 505–516 (2021).
Google Scholar
Brouqui, P. et al. Asymptomatic hypoxia in COVID-19 is associated with poor outcome. Int. J. Infect. Dis. 102, 233–238 (2021).
Google Scholar
Struyf, T. et al. Cochrane COVID-19 Diagnostic Test Accuracy Group. Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID‐19. Cochrane Database Syst. Rev. (5) (2022).
Garrafa, E. et al. Early prediction of in-hospital death of covid-19 patients: A machine-learning model based on age, blood analyses, and chest x-ray score. Elife 10, e70640 (2021).
Google Scholar
Schalekamp, S. et al. Model-based prediction of critical illness in hospitalized patients with covid-19. Radiology 298(1), E46–E54 (2021).
Google Scholar
Soda, P. et al. Aiforcovid: Predicting the clinical outcomes in patients with covid-19 applying ai to chest-x-rays. An Italian multicentre study. Med. Image Anal. 74, 102216 (2021).
Google Scholar
Shen, B. et al. Initial chest radiograph scores inform covid-19 status, intensive care unit admission and need for mechanical ventilation. Clin. Radiol. 76(6), 473.e1-473.e7 (2021).
Google Scholar
Liu, Y. et al. Tumor heterogeneity assessed by texture analysis on contrast-enhanced CT in lung adenocarcinoma: Association with pathologic grade. Oncotarget 8(32), 53664–53674 (2017).
Google Scholar
Krizhevsky, A., Sutskever, I. & Hinton, G. E. Imagenet classification with deep convolutional neural networks. Adv. Neural. Inf. Process. Syst. 25, 1–9 (2012).
He, K. et al. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778 (2016).
Chandra, T. B. et al. Coronavirus disease (covid19) detection in chest x-ray images using majority voting based classifier ensemble. Expert Syst. Appl. 165(113), 909 (2021).
Johri, S. et al. A novel machine learning-based analytical framework for automatic detection of covid-19 using chest x-ray images. Int. J. Imaging Syst. Technol. 31(3), 1105–1119 (2021).
Google Scholar
Selvi, J. T., Subhashini, K. & Methini, M. Investigation of covid-19 chest x-ray images using texture features—A comprehensive approach. Computational 1, 45–58 (2021).
Google Scholar
van Griethuysen, J. J. M. et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 77(21), e104–e107 (2017).
Google Scholar
Zhang, Q., Wu, Y. N. & Zhu, S. C. Interpretable convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 8827–8836 (2018).
Varghese, B. A. et al. Predicting clinical outcomes in covid-19 using radiomics on chest radiographs. Br. J. Radiol. 94(1126), 20210221 (2021).
Google Scholar
Iori, M. et al. Mortality prediction of COVID-19 patients using radiomic and neural network features extracted from a wide chest X-ray sample size: A robust approach for different medical imbalanced scenarios. Appl. Sci. 12(8), 3903 (2022).
Google Scholar
Blain, M. et al. Determination of disease severity in covid-19 patients using deep learning in chest x-ray images. Diagn. Interv. Radiol. 27(1), 20–27 (2021).
Google Scholar
Liu, X. et al. Temporal radiographic changes in covid-19 patients: Relationship to disease severity and viral clearance. Sci. Rep. 10(1), 10263 (2020).
Google Scholar
Yasin, R. & Gouda, W. Chest x-ray findings monitoring covid-19 disease course and severity. Egypt. J. Radiol. Nucl. Med. 51(1), 193 (2020).
Google Scholar
Castelli, G. et al. Brief communication: Chest radiography score in young covid-19 patients: Does one size fit all?. PLoS ONE 17(2), e0264172 (2022).
Google Scholar
Quan, H. et al. Coding algorithms for defining comorbidities in icd-9-cm and icd-10 administrative data. Med. Care 43(11), 1130–1139 (2005).
Google Scholar
Cau, R. et al. Long-COVID diagnosis: From diagnostic to advanced AI-driven models. Eur. J. Radiol. 148, 110164 (2022).
Google Scholar
Sudre, C. H. et al. Attributes and predictors of long COVID. Nat. Med. 27(4), 626–631 (2021).
Google Scholar
Jiao, Z. et al. Prognostication of patients with covid-19 using artificial intelligence based on chest x-rays and clinical data: A retrospective study. Lancet Digit. Health 3(5), e286–e294 (2021).
Google Scholar
Alqahtani, J. S. et al. Prevalence, severity and mortality associated with copd and smoking in patients with covid-19: A rapid systematic review and meta-analysis. PLoS ONE 15(5), e0233147 (2020).
Google Scholar
Centers for Disease Control and Prevention. Scientific evidence for conditions associated with higher risk for severe covid-19. https://www.cdc.gov/coronavirus/2019-ncov/science/sciencebriefs/underlying-evidence-table.html (Accessed 13 June 2022) (2022).
Ebinger, J. E. et al. Pre-existing traits associated with covid-19 illness severity. PLoS ONE 15(7), e0236240 (2020).
Google Scholar
Jordan, R. E., Adab, P. & Cheng, K. K. Covid-19: Risk factors for severe disease and death. BMJ 368, m1198 (2020).
Google Scholar
Khan, M. M. A. et al. Effects of underlying morbidities on the occurrence of deaths in covid-19 patients: A systematic review and meta-analysis. J. Glob. Health 10(2), 020503 (2020).
Google Scholar
Kim, L. et al. Risk factors for intensive care unit admission and in-hospital mortality among hospitalized adults identified through the us coronavirus disease 2019 (covid-19)-associated hospitalization surveillance network (covid-net). Clin. Infect. Dis. 72(9), e206–e214 (2021).
Google Scholar
Mikami, T. et al. Risk factors for mortality in patients with covid-19 in new york city. J. Gen. Intern. Med. 36(1), 17–26 (2021).
Google Scholar
Rod, J. E., Oviedo-Trespalacios, O. & Cortes-Ramirez, J. A brief-review of the risk factors for COVID-19 severity. Rev. Saude Publica 54, 60 (2020).
Google Scholar
Salerno, S. et al. Comprehensive evaluation of covid-19 patient short-and long-term outcomes: Disparities in healthcare utilization and post-hospitalization outcomes. PLoS ONE 16(10), e0258278 (2021).
Google Scholar
Ssentongo, P. et al. Association of cardiovascular disease and 10 other pre-existing comorbidities with covid-19 mortality: A systematic review and meta-analysis. PLoS ONE 15(8), e0238215 (2020).
Google Scholar
Wang, B. et al. Does comorbidity increase the risk of patients with covid-19: Evidence from meta-analysis. Aging 12(7), 6049–6057 (2020).
Google Scholar
Williamson, E. J. et al. Factors associated with covid-19-related death using opensafely. Nature 584(7821), 430–436 (2020).
Google Scholar
Wu, Z. & McGoogan, J. M. Characteristics of and important lessons from the coronavirus disease 2019 (covid-19) outbreak in China: Summary of a report of 72314 cases from the chinese center for disease control and prevention. JAMA 323(13), 1239–1242 (2020).
Google Scholar
Yang, J. et al. Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: A systematic review and meta-analysis. Int. J. Infect. Dis. 94, 91–95 (2020).
Google Scholar
Clarke, P. & Melendez, R. (Ann Arbor, MI) National neighborhood data archive (nanda): Neighborhood socioeconomic and demographic characteristics of census tracts, united states, 2000–2010. Inter-university Consortium for Political and Social Research.
Gu, T. et al. Characteristics associated with racial/ethnic disparities in covid-19 outcomes in an academic health care system. JAMA Netw. Open 3(10), e2025197 (2020).
Google Scholar
Salerno, S. et al. Patterns of repeated diagnostic testing for covid-19 in relation to patient characteristics and outcomes. J. Intern. Med. 289(5), 726–737 (2021).
Google Scholar
Crabb, B. T. et al. Comparison of international classification of diseases and related health problems, tenth revision codes with electronic medical records among patients with symptoms of coronavirus disease 2019. JAMA Netw. Open 3(8), e2017703 (2020).
Google Scholar
Elixhauser, A. et al. Comorbidity measures for use with administrative data. Med. Care 36(1), 8–27 (1998).
Google Scholar
van Walraven, C. et al. A modification of the elixhauser comorbidity measures into a point system for hospital death using administrative data. Med. Care 47(6), 626–633 (2009).
Google Scholar
Jain, A. K. Fundamentals of Digital Image Processing (Prentice-Hall Inc, 1989).
Google Scholar
Yip, S. S. & Aerts, H. J. Applications and limitations of radiomics. Phys. Med. Biol. 61(13), R150 (2016).
Google Scholar
Galloway, M. M. Texture analysis using gray level run lengths. Comput. Graph. Image Process. 4(2), 172–179 (1975).
Google Scholar
Chu, A., Sehgal, C. M. & Greenleaf, J. F. Use of gray value distribution of run lengths for texture analysis. Pattern Recogn. Lett. 11(6), 415–419 (1990).
Google Scholar
Haralick, R. M., Shanmugam, K. & Dinstein, I. Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC 3(6), 610–621 (1973).
Google Scholar
Thibault, G. et al. Shape and texture indexes application to cell nuclei classification. Int. J. Pattern Recogn. Artif. Intell. 27(01), 1357002 (2013).
Google Scholar
Fisher, A., Rudin, C. & Dominici, F. All models are wrong, but many are useful: Learning a variable’s importance by studying an entire class of prediction models simultaneously. J. Mach. Learn. Res. 20(177), 1–81 (2019).
Google Scholar
Ishwaran, H. et al. Random survival forests for high-dimensional data. Stat. Anal. Data Min. ASA Data Sci. J. 4(1), 115–132 (2011).
Google Scholar
Salerno, S. & Li, Y. High-dimensional survival analysis: Methods and applications. https://doi.org/10.48550/arXiv.2205.02948 (Preprint posted online May 5, 2022).
Uno, H. et al. Evaluating prediction rules for t-year survivors with censored regression models. J. Am. Stat. Assoc. 102(478), 527–537 (2007).
Google Scholar
Uno, H. et al. On the c-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Stat. Med. 30(10), 1105–1117 (2011).
Google Scholar
Breiman, L. Random forests. Mach. Learn. 45(1), 5–32 (2001).
Google Scholar
