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

Background: When 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 methods: We 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. Results: The 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. Conclusion: In 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.

[Effectiveness Estimation of Interventions against COVID-19 Using AI] AIによるCOVID-19感染抑制策の効果推定

The interventions implemented in each region to prevent the spread of COVID-19 depend on a great many factors. In order to estimate the effects of the interventions, a large number of those factors need to be analyzed in combination. Fujitsu Laboratories have developed Wide Learning which can search for combinations of data items exhaustively and rapidly to find out important combinations. Using this Wide Learning, we analyzed the success or failure of the interventions in each region through August 2020. Furthermore, based on the results, we use a what-if analysis to estimate the effect of each intervention in each region and at each point in time.