Data Science Techniques for COVID-19 in Intensive Care Units - Muñoz Lezcano, Sergio; López Hernández, Fernando Carlos; Corbi, Alberto (International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), 2022)
Data scientists aim to provide techniques and tools to the clinicians to manage the new coronavirus disease. Nowadays, new emerging tools based on Artificial Intelligence (AI), Image Processing (IP) and Machine Learning (ML) are contributing to the improvement of healthcare and treatments of different diseases. This paper reviews the most recent research efforts and approaches related to these new data driven techniques and tools in combination with the exploitation of the already available COVID-19 datasets. The tools can assist clinicians and nurses in efficient decision making with complex and heavily heterogeneous data, even in hectic and overburdened Intensive Care Units (ICU) scenarios. The datasets and techniques underlying these tools can help finding a more correct diagnosis. The paper also describes how these innovative AI+IP+ML-based methods (e.g., conventional X-ray imaging, clinical laboratory data, respiratory monitoring and automatic adjustments, etc.) can assist in the process of easing both the care of infected patients in ICUs and Emergency Rooms and the discovery of new treatments (drugs).
Keywords: coronavirus COVID-19; data mining; machine learning; image processing; biomarkers; X-ray; IJIMAI