Development and Validation of a Diagnostic Nomogram to Predict COVID-19 Pneumonia

Wang, Zhiyi; Weng, Jie; Li, Zhongwang; Hou, Ruonan; Zhou, Lebin; Ye, Hua; Chen, Ying; Yang, Ting; Chen, Daqing; Wang, Liang; Liu, Xiaodong; Shen, Xian; Jin, Shengwei

doi:10.1101/2020.04.03.20052068

Cited by 12 publications

(14 citation statements)

References 35 publications

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“…To date, many studies have reported the detection of COVID-19 in patients suspected of infection. 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 For instance, Menni et al. 16 reported the efficiency of self-reported symptoms in early identification of potential COVID-19.…”

Section: Discussionmentioning

confidence: 99%

A Learning-Based Model to Evaluate Hospitalization Priority in COVID-19 Pandemics

Zheng¹,

Zhu²,

Ji³

et al. 2020

Patterns

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The emergence of the novel coronavirus disease 2019 (COVID-19) is placing an increasing burden on healthcare systems. Although the majority of infected patients experience non-severe symptoms and can be managed at home, some individuals develop severe symptoms and require hospital admission. Therefore, it is critical to efficiently assess the severity of COVID-19 and identify hospitalization priority with precision. In this respect, a four-variable assessment model, including lymphocyte, lactate dehydrogenase, C-reactive protein, and neutrophil, is established and validated using the XGBoost algorithm. This model is found to be effective in identifying severe COVID-19 cases on admission, with a sensitivity of 84.6%, a specificity of 84.6%, and an accuracy of 100% to predict the disease progression toward rapid deterioration. It also suggests that a computation-derived formula of clinical measures is practically applicable for healthcare administrators to distribute hospitalization resources to the most needed in epidemics and pandemics.

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Section: Discussionmentioning

confidence: 99%

A Learning-Based Model to Evaluate Hospitalization Priority in COVID-19 Pandemics

Zheng¹,

Zhu²,

Ji³

et al. 2020

Patterns

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“…None of the BioRxiv posts were relevant to our topic. Of the 137 pre‐prints in MedRxiv, 23 nonduplicate studies included information on asthma, 29‐51 but none of them included specific information in children.…”

Section: Resultsmentioning

confidence: 99%

Asthma and COVID‐19 in children: A systematic review and call for data

Castro-Rodriguez

Forno

2020

Pediatric Pulmonology

108

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Rationale Whether asthma constitutes a risk factor for coronavirus disease‐2019 (COVID‐19) is unclear. Here, we aimed to assess whether asthma, the most common chronic disease in children, is associated with higher COVID‐19 risk or severity in pediatric populations. Methods We performed a systematic literature search in three stages: first, we reviewed PubMed, EMBASE, and CINAHL for systematic reviews of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) and COVID‐19 in pediatric populations, and reviewed their primary articles; second, we searched PubMed for studies on COVID‐19 or SARS‐CoV‐2 and asthma/wheeze, and evaluated whether the resulting studies included pediatric populations; third, we repeated the second search in http://BioRxiv.org and http://MedRxiv.org to find pre‐prints that may have information on pediatric asthma. Results In the first search, eight systematic reviews were found, of which five were done in pediatric populations; none of the 67 primary studies included data on pediatric asthma as a comorbidity for COVID‐19. In the second search, we found 34 results in PubMed, of which five reported asthma in adults, but none included data on children. In the third search, 25 pre‐prints in MedRxiv included data on asthma, but none on children. We found one report by the US Centers for Disease Control and Prevention stating that 40/345 (~11.5%) children with data on chronic conditions had “chronic lung diseases including asthma,” and one from a tertiary hospital in New York that reported asthma in 11/46 (~23.9%) children hospitalized for COVID‐19. Conclusion There is scarcely any data on whether childhood asthma (or other pediatric respiratory diseases) constitute risk factors for SARS‐CoV‐2 infection or COVID‐19 severity. Studies are needed that go beyond counting the number of cases in the pediatric age range.

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“…Other examples of approaches that use machine learning techniques for processing CT scan and X-Ray images can be found in [353] , [354] , [355] , [356] , [357] , [358] , [359] , [360] , [361] , [362] , [363] , [364] , [365] , [366] , [367] , [368] , [369] , [370] , [371] .…”

Section: Chest Computed Tomography and X-ray Image Processingmentioning

confidence: 99%

Applications of artificial intelligence in battling against covid-19: A literature review

2021

Chaos, Solitons & Fractals

160

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Development and Validation of a Diagnostic Nomogram to Predict COVID-19 Pneumonia

Cited by 12 publications

References 35 publications

A Learning-Based Model to Evaluate Hospitalization Priority in COVID-19 Pandemics

A Learning-Based Model to Evaluate Hospitalization Priority in COVID-19 Pandemics

Asthma and COVID‐19 in children: A systematic review and call for data

Applications of artificial intelligence in battling against covid-19: A literature review

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