ABSTRACT
For decades artificial intelligence (AI) has been used for various applications in the healthcare industry. Machine learning and artificial intelligence algorithms allow us to diagnose and customize medical care and follow-up plans to get better results, and during the covid19 pandemic, it was found that AI models have been using to predict the Covid-19 symptoms, understanding how it spreads, speeding up research and treatment using medical data. However, it is very challenging to make a robust AI model and use it in a real-time and real-world environment since most organizations do not want to share their data with other third parties due to privacy concerns, furthermore, it is difficult to build a generalized prediction model because of the fragmented nature of the patient data across the healthcare system. To solve the above problems, this paper presents a solution based on blockchain and AI technologies. The blockchain will securely protect the data access and AI-based federated learning for building a robust model for global and real-time usage. © 2022 Global IT Research Institute-GiRI.
ABSTRACT
For decades artificial intelligence (AI) has been used for various applications in the healthcare industry. Machine learning and artificial intelligence algorithms allow us to diagnose and customize medical care and follow-up plans to get better results, and during the covid19 pandemic, it was found that AI models have been using to predict the Covid-19 symptoms, understanding how it spreads, speeding up research and treatment using medical data. However, it is very challenging to make a robust AI model and use it in a real-time and real-world environment since most organizations do not want to share their data with other third parties due to privacy concerns, furthermore, it is difficult to build a generalized prediction model because of the fragmented nature of the patient data across the healthcare system. To solve the above problems, this paper presents a solution based on blockchain and AI technologies. The blockchain will securely protect the data access and AI-based federated learning for building a robust model for global and real-time usage.
ABSTRACT
OBJECTIVE: Recently, two influential articles that reported the association of (hydroxy)chloroquine or angiotensin converting enzyme (ACE) inhibitors and coronavirus disease 2019 (COVID-19) mortality were retracted due to significant methodological issues. Therefore, we aimed to analyze the same clinical issues through an improved research method and to find out the differences from the retracted papers. We systematically reviewed pre-existing literature, and compared the results with those of the retracted papers to gain a novel insight. MATERIALS AND METHODS: We extracted common risk factors identified in two retracted papers, and conducted relevant publication search until June 26, 2020 in PubMed. Then, we analyzed the risk factors for COVID-19 mortality and compared them to those of the retracted papers. RESULTS: Our systematic review demonstrated that most demographic and clinical risk factors for COVID-19 mortality were similar to those of the retracted papers. However, while the retracted paper indicated that both (hydroxy)chloroquine monotherapy and combination therapy with macrolide were associated with higher risk of mortality, our study showed that only combination therapy of hydroxychloroquine and macrolide was associated with higher risk of mortality (odds ratio 2.33; 95% confidence interval 1.63-3.34). In addition, our study demonstrated that use of ACE inhibitors or angiotensin receptor blockers (ARBs) was associated with reduced risk of mortality (0.77; 0.65-0.91). CONCLUSIONS: When analyzing the same clinical issues with the two retracted papers through a systematic review of randomized controlled trials and relevant cohort studies, we found out that (hydroxy)chloroquine monotherapy was not associated with higher risk of mortality, and that the use of ACE inhibitors or ARBs was associated with reduced risk of mortality in COVID-19 patients.