Analyzing factors contributing to COVID‐19 mortality in the United States using artificial intelligence techniques

Abstract

Having started since late 2019, COVID‐19 has spread through far many nations around the globe. Not being known profoundly, the novel virus of the Coronaviruses family has already caused more than half a million deaths and put the lives of many more people in danger. Policymakers have implemented preventive measures to curb the outbreak of the virus, and health practitioners along with epidemiologists have pointed out many social and hygienic factors associated with the virus incidence and mortality. However, a clearer vision of how the various factors cited hitherto can affect total death in different communities is yet to be analyzed. This study has put this issue forward. Applying artificial intelligence techniques, the relationship between COVID‐19 death toll and determinants mentioned as strongly influential in earlier studies was investigated. In the first stage, employing Best‐Worst Method, the weight of the primer contributing factor, effectiveness of strategies, was estimated. Then, using an integrated Best‐Worst Method–local linear neuro‐fuzzy–adaptive neuro‐fuzzy inference system approach, the relationship between COVID‐19 mortality rate and all factors namely effectiveness of strategies, age pyramid, health system status, and community health status was elucidated more specifically.

Effective Utilization of Data for Predicting COVID-19 Dynamics: An Exploration through Machine Learning Models

“…The proportion of vaccines vaccinated with one, two, and three doses differ Mathematical models have become a good tool for predicting the development of the COVID-19 pandemic and helping to make adequate management decisions to contain the pandemic. Various models have been developed [37][38][39]. However, each of them had some drawbacks, such as the impossibility of taking into account all the factors that negatively or positively affect the development of the COVID-19 epidemic process, that does not take into account the heterogeneity of the human population and differences in the structure of the population in different territories, etc.…”

Artificial Intelligence in Cardiac Surgery: A Systematic Review

“…It is worth noting that half of the included studies were from the United States, which suggests that developed countries are in the forefront of AI application in health care. Recently, the AI algorithms were being used in analyzing factors contributing to COVID-19 mortality and detection of pathological findings (40,41). In cardiac surgery, ML algorithms were used to predict mortality, survival, postoperative length of stay, and outcomes in following cardiac surgeries such as valve replacement, coronary artery bypass graft surgery, and heart transplantation (10,12,16,18,38,42).…”