"A Deep Learning Approach for Forecasting COVID-19 Patient Outcomes"

Abstract

"Deep learning" is a subfield of artificial intelligence that uses artificial neural networks, a machine learning technique, to comb through massive amounts of data in quest of patterns and predictions. In recent years, it has been characterised by rapid advancement and broad, successful application across numerous domains. Healthcare transformation faces a major challenge in extracting actionable insights from complex, multi-dimensional, and varied biological data. An overview of deep learning research on COVID-19, a summary of recent advances in the field, and a look at some practical uses of deep learning algorithms for COVID-19 diagnosis, prognosis, and treatment management are the goals of this study. Deep learning (DL) has the ability to improve the efficiency and accuracy of drug development by evaluating medical imaging data, laboratory test results, and other relevant data to diagnose, evaluate the progression and prognosis of diseases, and even provide treatment recommendations and medication use regimens. Furthermore, it could help legislators develop efficient control and prevention measures. Furthermore, we assess the current capabilities and limitations of deep learning in relation to the accuracy of COVID-19 treatments. This evaluation covers topics such as the lack of phenotypically abundant data and the need for deeper learning models that are more user-friendly. We conclude by discussing ways to overcome the current barriers that prevent deep learning from being fully utilised in future clinical applications.