Safety and Prevention Measure to Reduce the Spread of Corona Virus at Places of Mass Human Navigation-A precautious way to Protect from Covid-19

Ahmed, Syed Musthak; Rushitha, Sai; Neeraj,; Prabhath,; Swapna,; Gunjan, Vinit Kumar

doi:10.1007/978-3-030-96634-8_30

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…During the ongoing coronavirus disease 2019 (COVID-19) pandemic, researchers have shown the effectiveness of ML in various fields ( 32 – 34 ); however, potential applications of ML for disease prevention are unclear in real-world medical settings. Furthermore, translational bioinformatics in COVID-19 research has suggested the effectiveness of ML models with customized designs ( 35 ).…”

Section: Introductionmentioning

confidence: 99%

Predicting oxygen requirements in patients with coronavirus disease 2019 using an artificial intelligence-clinician model based on local non-image data

et al. 2022

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BackgroundWhen facing unprecedented emergencies such as the coronavirus disease 2019 (COVID-19) pandemic, a predictive artificial intelligence (AI) model with real-time customized designs can be helpful for clinical decision-making support in constantly changing environments. We created models and compared the performance of AI in collaboration with a clinician and that of AI alone to predict the need for supplemental oxygen based on local, non-image data of patients with COVID-19.Materials and methodsWe enrolled 30 patients with COVID-19 who were aged >60 years on admission and not treated with oxygen therapy between December 1, 2020 and January 4, 2021 in this 50-bed, single-center retrospective cohort study. The outcome was requirement for oxygen after admission.ResultsThe model performance to predict the need for oxygen by AI in collaboration with a clinician was better than that by AI alone. Sodium chloride difference >33.5 emerged as a novel indicator to predict the need for oxygen in patients with COVID-19. To prevent severe COVID-19 in older patients, dehydration compensation may be considered in pre-hospitalization care.ConclusionIn clinical practice, our approach enables the building of a better predictive model with prompt clinician feedback even in new scenarios. These can be applied not only to current and future pandemic situations but also to other diseases within the healthcare system.

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Section: Introductionmentioning

confidence: 99%